Journal articles: 'Malicious behavior pattern'

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Seo, Jungwoo, and Sangjin Lee. "Abnormal Behavior Detection to Identify Infected Systems Using the APChain Algorithm and Behavioral Profiling." Security and Communication Networks 2018 (September 4, 2018): 1–24. http://dx.doi.org/10.1155/2018/9706706.

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Recent cyber-attacks have used unknown malicious code or advanced attack techniques, such as zero-day attacks, making them extremely difficult to detect using traditional intrusion detection systems. Botnet attacks, for example, are a very sophisticated type of cyber-security threat. Malicious code or vulnerabilities are used to infect endpoints. Systems infected with this malicious code connect a communications channel to a command and control (C&C) server and receive commands to perform attacks on target servers. To effectively protect a corporate network’s resources against such threats, we must be able to detect infected systems before an attack occurs. In this paper, an attack pattern chain algorithm (APChain) is proposed to identify infected systems in real-time network environments, and a methodology for detecting abnormal behavior through network-based behavioral profiling is explained. APChain analyzes the attribute information of real-time network traffic, connects chains over time, and conducts behavioral profiling of different attack types to detect abnormal behavior. The dataset used in the experiment employed real-time traffic accumulated over a period of six months, and the proposed algorithm was developed into a prototype for the experiment. The C&C channel detection accuracy was measured at 0.996, the true positive rate at 1.0, and the false positive rate at 0.003. This study proposes a methodology that can overcome the limitations of conventional security mechanisms and suggests an approach to the detection of abnormal behavior in a real-time network environment.

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Khan, Abdul Karim, Chris M. Bell, and Samina Quratulain. "The two faces of envy: perceived opportunity to perform as a moderator of envy manifestation." Personnel Review 46, no. 3 (April 3, 2017): 490–511. http://dx.doi.org/10.1108/pr-12-2014-0279.

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Purpose The purpose of this paper is to investigate, with a Pakistani sample, the destructive and constructive behavioral intentions associated with benign and malicious envy in the context of perceived opportunity to perform. Design/methodology/approach The authors conducted two cross-sectional studies to test the hypotheses. In Study 1, data were obtained from students (n=90), whereas in Study 2, the authors used an executive sample (n=83). Findings The primary motivation of benign envy was to bring oneself up by improving performance on the comparison dimension, whereas the primary motive of malicious envy was to pull the envied other down. The relationship between malicious envy and behavioral “pulling down” intentions of derogating envied other was conditional on perceived opportunity on the comparison dimension. Consistent with a motive to improve self-evaluation, this study also found that perceived opportunity to perform interacted with benign envy to promote performance intentions on an alternative dimension. Furthermore, malicious envy was also associated with self-improving performance intentions on the comparison dimension, conditional upon perceived opportunity to perform. Practical implications Envy, depending on its nature, can become a positive or negative force in organizational life. The pattern of effects for opportunity structure differs from previous findings on control. The negative and positive effects of malicious envy may be managed by attention to opportunity structures. Originality/value This study supports the proposition that benign envy and malicious envy are linguistically and conceptually distinct phenomena, and it is the first to do so in a sample from Pakistan, a non-western and relatively more collectivistic culture. The authors also showed that negative and hostile envy-based behaviors are conditional upon the perceived characteristics of the context.

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Song, Chongya, Alexander Pons, and Kang Yen. "AA-HMM: An Anti-Adversarial Hidden Markov Model for Network-Based Intrusion Detection." Applied Sciences 8, no. 12 (November 28, 2018): 2421. http://dx.doi.org/10.3390/app8122421.

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In the field of network intrusion, malware usually evades anomaly detection by disguising malicious behavior as legitimate access. Therefore, detecting these attacks from network traffic has become a challenge in this an adversarial setting. In this paper, an enhanced Hidden Markov Model, called the Anti-Adversarial Hidden Markov Model (AA-HMM), is proposed to effectively detect evasion pattern, using the Dynamic Window and Threshold techniques to achieve adaptive, anti-adversarial, and online-learning abilities. In addition, a concept called Pattern Entropy is defined and acts as the foundation of AA-HMM. We evaluate the effectiveness of our approach employing two well-known benchmark data sets, NSL-KDD and CTU-13, in terms of the common performance metrics and the algorithm’s adaptation and anti-adversary abilities.

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Dhiyanesh, B., and S. Sakthivel. "UBP-Trust: User Behavioral Pattern Based Secure Trust Model for Mitigating Denial of Service Attacks in Software as a Service (SaaS) Cloud Environment." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 7649–54. http://dx.doi.org/10.1166/jctn.2016.5766.

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The problem of security enforcement in cloud environment has been discussed in number of situations and the most approaches uses minimum number of features to mitigate the denial of service attacks in cloud environment. The methods suffers with the problem of poor detection accuracy and false classification ratio, to overcome the issue, we propose a novel approach to mitigate the denial of service attacks in SaaS layer of cloud environment. This paper discusses a UBP-Trust model, which monitors the behavioral patterns of the users of cloud environment at different situations. Based on the monitored results, the method generates user behavior pattern which represents, the number of times the user has accessed the service, the number of times the service has been accessed and finished successfully, the amount of data being sent, the number of false invocation, the variance of protocol and so on. Using all these features considered the method generates the behavioral pattern and used to compute the user trust weight for each user being monitored. Based on the weight computed, he will be decided as malicious or genuine and based on which the method restrict the user from accessing the service. The proposed method produces efficient results in DDOS detection accuracy and produces less time complexity and false classification ratio.

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Sureda Riera, Tomás, Juan-Ramón Bermejo Higuera, Javier Bermejo Higuera, José-Javier Martínez Herraiz, and Juan-Antonio Sicilia Montalvo. "Prevention and Fighting against Web Attacks through Anomaly Detection Technology. A Systematic Review." Sustainability 12, no. 12 (June 17, 2020): 4945. http://dx.doi.org/10.3390/su12124945.

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Numerous techniques have been developed in order to prevent attacks on web servers. Anomaly detection techniques are based on models of normal user and application behavior, interpreting deviations from the established pattern as indications of malicious activity. In this work, a systematic review of the use of anomaly detection techniques in the prevention and detection of web attacks is undertaken; in particular, we used the standardized method of a systematic review of literature in the field of computer science, proposed by Kitchenham. This method is applied to a set of 88 papers extracted from a total of 8041 reviewed papers, which have been published in notable journals. This paper discusses the process carried out in this systematic review, as well as the results and findings obtained to identify the current state of the art of web anomaly detection.

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Soleymani, Ali, and Fatemeh Arabgol. "A Novel Approach for Detecting DGA-Based Botnets in DNS Queries Using Machine Learning Techniques." Journal of Computer Networks and Communications 2021 (July 5, 2021): 1–13. http://dx.doi.org/10.1155/2021/4767388.

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In today’s security landscape, advanced threats are becoming increasingly difficult to detect as the pattern of attacks expands. Classical approaches that rely heavily on static matching, such as blacklisting or regular expression patterns, may be limited in flexibility or uncertainty in detecting malicious data in system data. This is where machine learning techniques can show their value and provide new insights and higher detection rates. The behavior of botnets that use domain-flux techniques to hide command and control channels was investigated in this research. The machine learning algorithm and text mining used to analyze the network DNS protocol and identify botnets were also described. For this purpose, extracted and labeled domain name datasets containing healthy and infected DGA botnet data were used. Data preprocessing techniques based on a text-mining approach were applied to explore domain name strings with n-gram analysis and PCA. Its performance is improved by extracting statistical features by principal component analysis. The performance of the proposed model has been evaluated using different classifiers of machine learning algorithms such as decision tree, support vector machine, random forest, and logistic regression. Experimental results show that the random forest algorithm can be used effectively in botnet detection and has the best botnet detection accuracy.

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Heigl, Michael, Enrico Weigelt, Andreas Urmann, Dalibor Fiala, and Martin Schramm. "Exploiting the Outcome of Outlier Detection for Novel Attack Pattern Recognition on Streaming Data." Electronics 10, no. 17 (September 4, 2021): 2160. http://dx.doi.org/10.3390/electronics10172160.

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Future-oriented networking infrastructures are characterized by highly dynamic Streaming Data (SD) whose volume, speed and number of dimensions increased significantly over the past couple of years, energized by trends such as Software-Defined Networking or Artificial Intelligence. As an essential core component of network security, Intrusion Detection Systems (IDS) help to uncover malicious activity. In particular, consecutively applied alert correlation methods can aid in mining attack patterns based on the alerts generated by IDS. However, most of the existing methods lack the functionality to deal with SD data affected by the phenomenon called concept drift and are mainly designed to operate on the output from signature-based IDS. Although unsupervised Outlier Detection (OD) methods have the ability to detect yet unknown attacks, most of the alert correlation methods cannot handle the outcome of such anomaly-based IDS. In this paper, we introduce a novel framework called Streaming Outlier Analysis and Attack Pattern Recognition, denoted as SOAAPR, which is able to process the output of various online unsupervised OD methods in a streaming fashion to extract information about novel attack patterns. Three different privacy-preserving, fingerprint-like signatures are computed from the clustered set of correlated alerts by SOAAPR, which characterizes and represents the potential attack scenarios with respect to their communication relations, their manifestation in the data’s features and their temporal behavior. Beyond the recognition of known attacks, comparing derived signatures, they can be leveraged to find similarities between yet unknown and novel attack patterns. The evaluation, which is split into two parts, takes advantage of attack scenarios from the widely-used and popular CICIDS2017 and CSE-CIC-IDS2018 datasets. Firstly, the streaming alert correlation capability is evaluated on CICIDS2017 and compared to a state-of-the-art offline algorithm, called Graph-based Alert Correlation (GAC), which has the potential to deal with the outcome of anomaly-based IDS. Secondly, the three types of signatures are computed from attack scenarios in the datasets and compared to each other. The discussion of results, on the one hand, shows that SOAAPR can compete with GAC in terms of alert correlation capability leveraging four different metrics and outperforms it significantly in terms of processing time by an average factor of 70 in 11 attack scenarios. On the other hand, in most cases, all three types of signatures seem to reliably characterize attack scenarios such that similar ones are grouped together, with up to 99.05% similarity between the FTP and SSH Patator attack.

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Lange, Jens, Delroy L. Paulhus, and Jan Crusius. "Elucidating the Dark Side of Envy: Distinctive Links of Benign and Malicious Envy With Dark Personalities." Personality and Social Psychology Bulletin 44, no. 4 (December 22, 2017): 601–14. http://dx.doi.org/10.1177/0146167217746340.

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Researchers have recently drawn a contrast between two forms of envy: benign and malicious envy. In three studies (total N = 3,123), we challenge the assumption that malicious envy is destructive, whereas benign envy is entirely constructive. Instead, both forms have links with the Dark Triad of personality. Benign envy is associated with Machiavellian behaviors, whereas malicious envy is associated with both Machiavellian and psychopathic behaviors. In Study 1, this pattern emerged from meta-analyzed trait correlations. In Study 2, a manipulation affecting the envy forms mediated an effect on antisocial behavioral intentions. Study 3 replicated these patterns by linking envy to specific antisocial behaviors and their impact on status in the workplace. Together, our correlational and experimental results suggest that the two forms of envy can both be malevolent. Instead of evaluating envy’s morality, we propose to focus on its functional value.

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Sikder, Amit Kumar, Leonardo Babun, and A. Selcuk Uluagac. "A egis +." Digital Threats: Research and Practice 2, no. 1 (March 2021): 1–33. http://dx.doi.org/10.1145/3428026.

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The introduction of modern Smart Home Systems (SHSs) is redefining the way we perform everyday activities. Today, myriad SHS applications and the devices they control are widely available to users. Specifically, users can easily download and install the apps from vendor-specific app markets, or develop their own, to effectively implement their SHS solutions. However, despite their benefits, app-based SHSs unfold diverse security risks. Several attacks have already been reported to SHSs and current security solutions only consider smart home devices and apps individually to detect malicious actions, rather than the context of the SHS as a whole. Thus, the current security solutions applied to SHSs cannot capture user activities and sensor-device-user interactions in a holistic fashion. To address these limitations, in this article, we introduce A egis +, a novel context-aware platform-independent security framework to detect malicious behavior in an SHS. Specifically, A egis + observes the states of the connected smart home entities (sensors and devices) for different user activities and usage patterns in an SHS and builds a contextual model to differentiate between malicious and benign behavior. We evaluated the efficacy and performance of A egis + in multiple smart home settings (i.e., single bedroom, double bedroom, duplex) and platforms (i.e., Samsung SmartThings, Amazon Alexa) where real users perform day-to-day activities using real SHS devices. We also measured the performance of A egis + against five different malicious behaviors. Our detailed evaluation shows that A egis + can detect malicious behavior in SHS with high accuracy (over 95%) and secure the SHS regardless of the smart home layout and platforms, device configurations, installed apps, controller devices, and enforced user policies. Finally, A egis + yields minimum overhead to the SHS, ensuring effective deployability in real-life smart environments.

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Yu, WangYang, Chun Gang Yan, ZhiJun Ding, ChangJun Jiang, and MengChu Zhou. "Modeling and Verification of Online Shopping Business Processes by Considering Malicious Behavior Patterns." IEEE Transactions on Automation Science and Engineering 13, no. 2 (April 2016): 647–62. http://dx.doi.org/10.1109/tase.2014.2362819.

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Aslan, Ömer, Refik Samet, and Ömer Özgür Tanrıöver. "Using a Subtractive Center Behavioral Model to Detect Malware." Security and Communication Networks 2020 (February 27, 2020): 1–17. http://dx.doi.org/10.1155/2020/7501894.

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In recent years, malware has evolved by using different obfuscation techniques; due to this evolution, the detection of malware has become problematic. Signature-based and traditional behavior-based malware detectors cannot effectively detect this new generation of malware. This paper proposes a subtractive center behavior model (SCBM) to create a malware dataset that captures semantically related behaviors from sample programs. In the proposed model, system paths, where malware behaviors are performed, and malware behaviors themselves are taken into consideration. This way malicious behavior patterns are differentiated from benign behavior patterns. Features that could not exceed the specified score are removed from the dataset. The datasets created using the proposed model contain far fewer features than the datasets created by n-gram and other models that have been used in other studies. The proposed model can handle both known and unknown malware, and the obtained detection rate and accuracy of the proposed model are higher than those of the known models. To show the effectiveness of the proposed model, 2 datasets with score and without score are created by using SCBM. In total, 6700 malware samples and 3000 benign samples are tested. The results are compared with those derived from n-gram and models from other studies in the literature. The test results show that, by combining the proposed model with an appropriate machine learning algorithm, the detection rate, false positive rate, and accuracy are measured as 99.9%, 0.2%, and 99.8%, respectively.

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Jang, Jae-wook, and Huy Kang Kim. "Function-Oriented Mobile Malware Analysis as First Aid." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/6707524.

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Recently, highly well-crafted mobile malware has arisen as mobile devices manage highly valuable and sensitive information. Currently, it is impossible to detect and prevent all malware because the amount of new malware continues to increase exponentially; malware detection methods need to improve in order to respond quickly and effectively to malware. For the quick response, revealing the main purpose or functions of captured malware is important; however, only few recent works have attempted to find malware’s main purpose. Our approach is designed to help with efficient and effective incident responses or countermeasure development by analyzing the main functions of malicious behavior. In this paper, we propose a novel method for function-oriented malware analysis approach based on analysis of suspicious API call patterns. Instead of extracting API call patterns for malware in each family, we focus on extracting such patterns for certain malicious functionalities. Our proposed method dumps memory sections where an application is allocated and extracts suspicious API sequences from bytecode by comparing with predefined suspicious API lists. By matching API call patterns with our functionality database, our method determines whether they are malicious. The experiment results demonstrate that our method performs well in detecting malware with high accuracy.

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Aridoss, Manimaran. "Defensive Mechanism Against DDoS Attack to Preserve Resource Availability for IoT Applications." International Journal of Handheld Computing Research 8, no. 4 (October 2017): 40–51. http://dx.doi.org/10.4018/ijhcr.2017100104.

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The major challenge of Internet of Things (IoT) generated data is its hypervisor level vulnerabilities. Malicious VM deployment and termination are so simple due to its multitenant shared nature and distributed elastic cloud features. These features enable the attackers to launch Distributed Denial of Service attacks to degrade cloud server performance. Attack detection techniques are applied to the VMs that are used by malicious tenants to hold the cloud resources by launching DDoS attacks at data center subnets. Traditional dataflow-based attack detection methods rely on the similarities of incoming requests which consist of IP and TCP header information flows. The proposed approach classifies the status patterns of malicious VMs and ideal VMs to identify the attackers. In this article, information theory is used to calculate the entropy value of the malicious virtual machines for detecting attack behaviors. Experimental results prove that the proposed system works well against DDoS attacks in IoT applications.

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Hall, Calum, Lynsay Shepherd, and Natalie Coull. "BlackWatch: Increasing Attack Awareness within Web Applications." Future Internet 11, no. 2 (February 15, 2019): 44. http://dx.doi.org/10.3390/fi11020044.

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Web applications are relied upon by many for the services they provide. It is essential that applications implement appropriate security measures to prevent security incidents. Currently, web applications focus resources towards the preventative side of security. While prevention is an essential part of the security process, developers must also implement a level of attack awareness into their web applications. Being able to detect when an attack is occurring provides applications with the ability to execute responses against malicious users in an attempt to slow down or deter their attacks. This research seeks to improve web application security by identifying malicious behavior from within the context of web applications using our tool BlackWatch. The tool is a Python-based application which analyzes suspicious events occurring within client web applications, with the objective of identifying malicious patterns of behavior. This approach avoids issues typically encountered with traditional web application firewalls. Based on the results from a preliminary study, BlackWatch was effective at detecting attacks from both authenticated and unauthenticated users. Furthermore, user tests with developers indicated BlackWatch was user-friendly, and was easy to integrate into existing applications. Future work seeks to develop the BlackWatch solution further for public release.

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JIAO, WENPIN. "ESTABLISHING MUTUAL-BELIEF AMONG COOPERATIVE AGENTS." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 08 (December 2002): 973–93. http://dx.doi.org/10.1142/s0218001402002118.

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Mutual-belief is one important premise to ensure that cooperation among multiple agents goes smoothly. However, mutual-belief among agents is also always taken for granted. In this paper, we adapt a method based on the position-exchange principle (PEP) to reason about mutual-belief among agents. By reasoning about mutual-belief among agents, we can judge whether cooperation among agents can go on rationally or not. However, if there are malicious agents involved in cooperation, the profit of honesty agents will be injured. To make cooperation useful, agents should be able to reason about cheating behaviors of malicious agents during cooperation. We extend the standard pi-calculus to specify the expectations of agents and define a group of criteria for anti-cheating that agents can use to establish true mutual-belief.

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Tao, Yufei. "Technical Perspective of Efficient Directed Densest Subgraph Discovery." ACM SIGMOD Record 50, no. 1 (June 15, 2021): 32. http://dx.doi.org/10.1145/3471485.3471493.

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The problem is useful in graph mining because dense subgraphs often represent patterns deserving special attention. They could indicate, for example, an authoritative community in a social network, a building brick of more complex biology structures, or even a type of malicious behavior such as spamming. See [1, 3] and the references therein for an extensive discussion on the applications of DDS.

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Ermagun, Alireza, and Nazanin Tajik. "Recovery patterns and physics of the network." PLOS ONE 16, no. 1 (January 19, 2021): e0245396. http://dx.doi.org/10.1371/journal.pone.0245396.

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In a progressively interconnected world, the loss of system resilience has consequences for human health, the economy, and the environment. Research has exploited the science of networks to explain the resilience of complex systems against random attacks, malicious attacks, and the localized attacks induced by natural disasters or mass attacks. Little is known about the elucidation of system recovery by the network topology. This study adds to the knowledge of network resilience by examining the nexus of recoverability and network topology. We establish a new paradigm for identifying the recovery behavior of networks and introduce the recoverability measure. Results indicate that the recovery response behavior and the recoverability measure are the function of both size and topology of networks. In small sized networks, the return to recovery exhibits homogeneous recovery behavior over topology, while the return shape is dispersed with an increase in the size of network. A network becomes more recoverable as connectivity measures of the network increase, and less recoverable as accessibility measures of network increase. Overall, the results not only offer guidance on designing recoverable networks, but also depict the recovery nature of networks deliberately following a disruption. Our recovery behavior and recoverability measure has been tested on 16 distinct network topologies. The relevant recovery behavior can be generalized based on our definition for any network topology recovering deliberately.

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Mamedova, Natalia, Arkadiy Urintsov, Olga Staroverova, Evgeniy Ivanov, and Dmitriy Galahov. "Social engineering in the context of ensuring information security." SHS Web of Conferences 69 (2019): 00073. http://dx.doi.org/10.1051/shsconf/20196900073.

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The paper presents the main key features of social engineering and a social engineer activity. Emphasis is placed on the study of social engineering techniques in the system of human-machine interaction used to implement the illegal (malicious) manipulation of human behavior patterns. The matrix of social engineering qualification criteria and the map of information security risks caused by social engineer actions were built.

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ZHOU, Huaizhe, Haihe BA, Yongjun WANG, and Tie HONG. "On the Detection of Malicious Behaviors against Introspection Using Hardware Architectural Events." IEICE Transactions on Information and Systems E103.D, no. 1 (January 1, 2020): 177–80. http://dx.doi.org/10.1587/transinf.2019edl8148.

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Xi, Xiangyu, Tong Zhang, Wei Ye, Zhao Wen, Shikun Zhang, Dongdong Du, and Qing Gao. "An Ensemble Approach for Detecting Anomalous User Behaviors." International Journal of Software Engineering and Knowledge Engineering 28, no. 11n12 (November 2018): 1637–56. http://dx.doi.org/10.1142/s0218194018400211.

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An intruder of a company’s network may use stolen login credentials to silently collect sensitive data. Such malicious user behavior is difficult to detect as long as it does not trigger access violation or data leak alert. In this paper, we propose to use an ensemble of three unsupervised anomaly detection algorithms, namely OCSVM, RNN and Isolation Forest, to detect abnormal user behavior patterns. Besides, an User Behavior Analytics (UBA) Platform is proposed to collect logs, extract features and conduct experiments. The experiment results indicate that our algorithm outperforms each individual algorithm with recall of 96.55% and precision of 91.24% on average, while both OCSVM and RNN suffer from anomalies in the training set, and [Formula: see text] produces more false positives and false negatives in prediction.

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Pertiwi, Nirmala Fajar, and Ice Yulia Wardani. "HARGA DIRI REMAJA DAN POLA ASUH ORANGTUA SEBAGAI FAKTOR PROTEKTIF IDE BUNUH DIRI." Jurnal Ilmiah Permas: Jurnal Ilmiah STIKES Kendal 9, no. 3 (July 17, 2019): 301–10. http://dx.doi.org/10.32583/pskm.9.3.2019.301-310.

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Remaja yang tidak mampu menghadapi tekanan akan membawanya pada ketidakstabilan emosional dan cenderung melakukan berbagai perilaku berbahaya hingga bunuh diri. Bunuh diri memiliki faktor protektif berupa harga diri tinggi karena dapat memberikan kualitas psikologis positif. Faktor protektif ide bunuh diri lainnya yaitu pola asuh yang seimbang antara dimensi penerimaan dan pengendalian atau disebut pola asuh otoritatif. Penelitian ini bertujuan untuk mengetahui hubungan harga diri dan pola asuh orangtua dengan ide bunuh diri pada remaja SMA. Desain penelitian ini menggunakan deskriptif korelatif dan pendekatan secara chross-sectional. Penelitian ini memiliki responden sejumlah 322 remaja di SMA yang dipilih dengan teknik proportional random sampling. Terdapat hubungan yang kuat dan arah negatifantara harga diri dengan ide bunuh diri dengan koefisien korelasi -0,876, yang berarti bahwa semakin rendah harga diri yang dimiliki remaja maka semakin tinggi ide bunuh diri. Terdapat hubungan dengan kekuatan sedangdan arah negatif antara pola asuh orangtua dengan ide bunuh diri dengan koefisien korelasi -0,365, artinya apabila pola asuh orangtua mengarah pada otoritatif maka ide bunuh diri akan semakin rendah, dan sebaliknya apabila pola asuh orangtua mengarah pada otoritarian maka ide bunuh diri akan semakin tinggi. Penelitian ini diharapkan dapat meningkatkan intervensi keperawatan jiwa dalammengidentifikasi ide bunuh diri pada remaja, serta meningkatkan wawasan remaja dan guru terkait faktor protektif ide bunuh diri. Kata kunci: faktor proteksi, harga diri,ide bunuh diri,dan pola asuh orangtua SELF-SELF-PRICE AND PARENT'S PATTERN AS SELF-KILLING IDEAS PROTECTIVE FACTORS ABSTRACT Teenagers who do not cope well under pressure will lead them to emotional instability and tend to perform a variety of malicious behavior or commit to suicide. Suicidial Ideation has protective factor such as high self esteem, because it can provide positive psychological qualities.Other protective factor is parenting style that contain balance between the dimensions of acceptance and control, also called authoritative. This study aims to determine the relationship of self-esteem and parenting Stylewith Suicidial Ideation in high school adolescents. This study used descriptive correlative and cross-sectional approach. This study has a number of 322 respondents, that are high school adolescents selected by proportional random sampling technique. There is strong relationshipwith negative directionbetween self-esteem with suicidal ideationand the correlation coefficient is -0,876, which means that the if adolescent’s self-esteem is lower so suicidal ideation will be higher. There is moderate relationshipwith negative direction between parenting style with suicidal ideation and the correlation coefficient is -0,365, which means that if parenting style is authoritative so suicidal ideation will be lower, and if parenting style is authoritarian so suicidal ideation will be higher.This study can be used to improve nursing intervention in identify suicidal ideation, and also to improve teenager’s and teacher’s knowledge about protective factors of suicidal ideation. Keywords: parenting style, protective factor,self-esteem and suicidal ideation

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Hu, Jinlong, Junjie Liang, and Shoubin Dong. "iBGP: A Bipartite Graph Propagation Approach for Mobile Advertising Fraud Detection." Mobile Information Systems 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/6412521.

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Online mobile advertising plays a vital financial role in supporting free mobile apps, but detecting malicious apps publishers who generate fraudulent actions on the advertisements hosted on their apps is difficult, since fraudulent traffic often mimics behaviors of legitimate users and evolves rapidly. In this paper, we propose a novel bipartite graph-based propagation approach, iBGP, for mobile apps advertising fraud detection in large advertising system. We exploit the characteristics of mobile advertising user’s behavior and identify two persistent patterns: power law distribution and pertinence and propose an automatic initial score learning algorithm to formulate both concepts to learn the initial scores of non-seed nodes. We propose a weighted graph propagation algorithm to propagate the scores of all nodes in the user-app bipartite graphs until convergence. To extend our approach for large-scale settings, we decompose the objective function of the initial score learning model into separate one-dimensional problems and parallelize the whole approach on an Apache Spark cluster. iBGP was applied on a large synthetic dataset and a large real-world mobile advertising dataset; experiment results demonstrate that iBGP significantly outperforms other popular graph-based propagation methods.

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Su, Han, Minglun Ren, Anning Wang, Xiaoan Tang, Xin Ni, and Zhao Fang. "A Behavior-Driven Forum Spammer Recognition Method with Its Application in Automobile Forums." Mathematical Problems in Engineering 2021 (August 30, 2021): 1–11. http://dx.doi.org/10.1155/2021/7682579.

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Forum comments are valuable information for enterprises to discover public preferences and market trends. However, extensive marketing and malicious attack behaviors in forums are always an obstacle for enterprises to make effective use of this information. And these forum spammers are constantly updating technology to prevent detection. Therefore, how to accurately recognize forum spammers has become an important issue. Aiming to accurately recognize forum spammers, this paper changes the research target from understanding abnormal reviews and the suspicious relationship among forum spammers to discover how they must behave (follow or be followed) to achieve their monetary goals. First, we classify forum spammers into automated forum spammers and marketing forum spammers based on different behavioral features. Then, we propose a support vector machine-based automated spammer recognition (ASR) model and a k-means clustering-based marketing spammer recognition (MSR) model. The experimental results on the real-world labelled dataset illustrate the effectiveness of our methods on classification spammer from common users. To the best of our knowledge, this work is among the first to construct behavior-driven recognition models according to the different behavioral patterns of forum spammers.

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Su, Ming-Yang, Hong-Siou Wei, Xin-Yu Chen, Po-Wei Lin, and Ding-You Qiu. "Using Ad-Related Network Behavior to Distinguish Ad Libraries." Applied Sciences 8, no. 10 (October 9, 2018): 1852. http://dx.doi.org/10.3390/app8101852.

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Mobile app ads pose a far greater security threat to users than adverts on computer browsers. This is because app developers must embed a Software Development Kit (SDK), called an ad library or ad lib for short, provided by ad networks (i.e., ad companies) into their app program, and then merge and compile it into an Android PacKage (APK) execution file. The ad lib thus becomes a part of the entire app, and shares the whole permissions granted to the app. Unfortunately, this also resulted in many security issues, such as ad libs abusing the permissions to collect and leak private data, ad servers redirecting ad requests to download malicious JavaScript from unknown servers to execute it in the background of the mobile operating system without the user’s consent. The more well-known an embedded ad lib, the safer the app may be, and vice versa. Importantly, while decompiling an APK to inspect its source code may not identify the ad lib(s), executing the app on a simulator can reveal the network behavior of the embedded ad lib(s). Ad libs exhibit different behavior patterns when communicating with ad servers. This study uses a dynamic analysis method to inspect an executing app, and plots the ad lib behavior patterns related to the advertisement into a graph. It is then determined whether or not the ad lib is from a trusted ad network using comparisons of graph similarities.

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Hyun, Sangwon, Junsung Cho, Geumhwan Cho, and Hyoungshick Kim. "Design and Analysis of Push Notification-Based Malware on Android." Security and Communication Networks 2018 (July 9, 2018): 1–12. http://dx.doi.org/10.1155/2018/8510256.

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Establishing secret command and control (C&C) channels from attackers is important in malware design. This paper presents design and analysis of malware architecture exploiting push notification services as C&C channels. The key feature of the push notification-based malware design is remote triggering, which allows attackers to trigger and execute their malware by push notifications. The use of push notification services as covert channels makes it difficult to distinguish this type of malware from other normal applications also using the same services. We implemented a backdoor prototype on Android devices as a proof-of-concept of the push notification-based malware and evaluated its stealthiness and feasibility. Our malware implementation effectively evaded the existing malware analysis tools such as 55 antimalware scanners from VirusTotal and SandDroid. In addition, our backdoor implementation successfully cracked about 98% of all the tested unlock secrets (either PINs or unlock patterns) in 5 seconds with only a fraction (less than 0.01%) of the total power consumption of the device. Finally, we proposed several defense strategies to mitigate push notification-based malware by carefully analyzing its attack process. Our defense strategies include filtering subscription requests for push notifications from suspicious applications, providing centralized management and access control of registration tokens of applications, detecting malicious push messages by analyzing message contents and characteristic patterns demonstrated by malicious push messages, and detecting malware by analyzing the behaviors of applications after receiving push messages.

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Kim, Sujeong, Chanwoong Hwang, and Taejin Lee. "Anomaly Based Unknown Intrusion Detection in Endpoint Environments." Electronics 9, no. 6 (June 20, 2020): 1022. http://dx.doi.org/10.3390/electronics9061022.

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According to a study by Cybersecurity Ventures, cybercrime is expected to cost $6 trillion annually by 2021. Most cybersecurity threats access internal networks through infected endpoints. Recently, various endpoint environments such as smartphones, tablets, and Internet of things (IoT) devices have been configured, and security issues caused by malware targeting them are intensifying. Event logs-based detection technology for endpoint security is detected using rules or patterns. Therefore, known attacks can respond, but unknown attacks can be difficult to respond to immediately. To solve this problem, in this paper, local outlier factor (LOF) and Autoencoder detect suspicious behavior that deviates from normal behavior. It also detects threats and shows the corresponding threats when suspicious events corresponding to the rules created through the attack profile are constantly occurring. Experimental results detected eight new suspicious processes that were not previously detected, and four malicious processes and one suspicious process were judged using Hybrid Analysis and VirusTotal. Based on the experiment results, it is expected that the use of operational policies such as allowlists in the proposed model will significantly improve performance by minimizing false positives.

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Yassin, Amr Hassan, and Hany Hamdy Hussien. "A Proposed Heuristic Optimization Algorithm for Detecting Network Attacks." Academic Research Community publication 2, no. 4 (January 1, 2019): 530. http://dx.doi.org/10.21625/archive.v2i4.397.

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Due to the exponential growth of E-Business and computing capabilities over the web for a pay-for-use groundwork, the risk factors regarding security issues also increase rapidly. As the usage increases, it becomes very difficult to identify malicious attacks since the attack patterns change. Therefore, host machines in the network must continually be monitored for intrusions since they are the final endpoint of any network. The purpose of this work is to introduce a generalized neural network model that has the ability to detect network intrusions. Two recent heuristic algorithms inspired by the behavior of natural phenomena, namely, the particle swarm optimization (PSO) and gravitational search (GSA) algorithms are introduced. These algorithms are combined together to train a feed forward neural network (FNN) for the purpose of utilizing the effectiveness of these algorithms to reduce the problems of getting stuck in local minima and the time-consuming convergence rate. Dimension reduction focuses on using information obtained from NSL-KDD Cup 99 data set for the selection of some features to discover the type of attacks. Detecting the network attacks and the performance of the proposed model are evaluated under different patterns of network data.

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Carpen-Amarie, Alexandra, Alexandru Costan, Jing Cai, Gabriel Antoniu, and Luc Bougé. "Bringing introspection into BlobSeer: Towards a self-adaptive distributed data management system." International Journal of Applied Mathematics and Computer Science 21, no. 2 (June 1, 2011): 229–42. http://dx.doi.org/10.2478/v10006-011-0017-y.

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Bringing introspection into BlobSeer: Towards a self-adaptive distributed data management system Introspection is the prerequisite of autonomic behavior, the first step towards performance improvement and resource usage optimization for large-scale distributed systems. In grid environments, the task of observing the application behavior is assigned to monitoring systems. However, most of them are designed to provide general resource information and do not consider specific information for higher-level services. More precisely, in the context of data-intensive applications, a specific introspection layer is required to collect data about the usage of storage resources, data access patterns, etc. This paper discusses the requirements for an introspection layer in a data management system for large-scale distributed infrastructures. We focus on the case of BlobSeer, a large-scale distributed system for storing massive data. The paper explains why and how to enhance BlobSeer with introspective capabilities and proposes a three-layered architecture relying on the MonALISA monitoring framework. We illustrate the autonomic behavior of BlobSeer with a self-configuration component aiming to provide storage elasticity by dynamically scaling the number of data providers. Then we propose a preliminary approach for enabling self-protection for the BlobSeer system, through a malicious client detection component. The introspective architecture has been evaluated on the Grid'5000 testbed, with experiments that prove the feasibility of generating relevant information related to the state and behavior of the system.

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Pudjiono, Moch Juli, Bambang Sukarjono, and Hery Sumanto. "Penyuluhan Hukum : Pencegahan Kenakalan Remaja Di Desa Ngujung Kecamatan Maospati Kabupaten Magetan." JURNAL DAYA-MAS 5, no. 1 (June 2, 2020): 27–31. http://dx.doi.org/10.33319/dymas.v5i1.38.

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This service aims to provide an understanding of juvenile delinquency problems. By using legal counseling methods. Location of community service in Ngujung Village, Maospati Subdistrict, Magetan District. Based on the results of the discussion show that adolescence is a period where an individual experiences a transition from one stage to the next and experiences changes in both emotions, body, interests, behavioral patterns, and also full of problems. While Juvenile delinquency (juvenile delinquency) is malicious behavior, or crime / delinquency of young people is a social pathology in children and adolescents caused by a form of social neglect, so they develop deviant forms of behavior. For this reason, efforts to prevent juvenile delinquency can be done, among others: (1) Parents must pay more attention, supervision, and affection to children and parents must open two-way communication (listening and open) to children, (2) Giving limits on freedom, (3) Providing religious education to adolescents, (4) Teaching adolescents not to be easily influenced by negative relationships, (5) Providing positive activities to adolescents so that adolescents are busy and do not have time to do things negative things and (6) Providing knowledge about laws that regulate juvenile delinquency and sanctions. Keywords—: Extension of Law; Juvenile Delinquency.

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Yakubu, Mohammed B., Hussaini DanAzumi, Mohammed Bulama, and Abba Hassan. "Intrusion tolerance model against higher institution database." Global Journal of Information Technology: Emerging Technologies 9, no. 1 (April 30, 2019): 20–28. http://dx.doi.org/10.18844/gjit.v9i1.4060.

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Privacy and security are the two major concerns of keeping and accessing data on the internet. The rate of intruding organisation’s database by unauthorised users is on the increase. Thus, the affected organisation’s data confidentiality is lost; it can be viewed, modified, deleted and/or make it inaccessible to authorised users. Intrusion detection and tolerance techniques help in recognising malicious attacks as well as supports the websites to survive the attack. A quantitative approach was used in this study even though numerous attempts of quantitative evaluation of the survivability of intrusion tolerant systems, especially in database field have been made. Study on survivability of intrusion tolerant systems has being done, taking behaviour of attack, prediction of scale, speed of database damage propagation and its degree of spreading as facilitators. This paper provides the intrusion tolerant database system as a series of state transition model (Zumkas Model) based on the hidden Markov model. Keywords: Intrusion, survivability, model, patterns

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Venkatraman, Sitalakshmi, and Mamoun Alazab. "Use of Data Visualisation for Zero-Day Malware Detection." Security and Communication Networks 2018 (December 2, 2018): 1–13. http://dx.doi.org/10.1155/2018/1728303.

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With the explosion of Internet of Things (IoT) worldwide, there is an increasing threat from malicious software (malware) attackers that calls for efficient monitoring of vulnerable systems. Large amounts of data collected from computer networks, servers, and mobile devices need to be analysed for malware proliferation. Effective analysis methods are needed to match with the scale and complexity of such a data-intensive environment. In today’s Big Data contexts, visualisation techniques can support malware analysts going through the time-consuming process of analysing suspicious activities thoroughly. This paper takes a step further in contributing to the evolving realm of visualisation techniques used in the information security field. The aim of the paper is twofold: (1) to provide a comprehensive overview of the existing visualisation techniques for detecting suspicious behaviour of systems and (2) to design a novel visualisation using similarity matrix method for establishing malware classification accurately. The prime motivation of our proposal is to identify obfuscated malware using visualisation of the extended x86 IA-32 (opcode) similarity patterns, which are hard to detect with the existing approaches. Our approach uses hybrid models wherein static and dynamic malware analysis techniques are combined effectively along with visualisation of similarity matrices in order to detect and classify zero-day malware efficiently. Overall, the high accuracy of classification achieved with our proposed method can be visually observed since different malware families exhibit significantly dissimilar behaviour patterns.

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Safar, Noor Zuraidin Mohd, Noryusliza Abdullah, Hazalila Kamaludin, Suhaimi Abd Ishak, and Mohd Rizal Mohd Isa. "Characterising and detection of botnet in P2P network for UDP protocol." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 3 (June 1, 2020): 1584. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1584-1595.

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Developments in computer networking have raised concerns of the associated Botnets threat to the Internet security. Botnet is an inter-connected computers or nodes that infected with malicious software and being controlled as a group without any permission of the computer’s owner. This paper explores how network traffic characterising can be used for identification of botnet at local networks. To analyse the characteristic, behaviour or pattern of the botnet in the network traffic, a proper network analysing tools is needed. Several network analysis tools available today are used for the analysis process of the network traffic. In the analysis phase, the botnet detection strategy based on the signature and DNS anomaly approach are selected to identify the behaviour and the characteristic of the botnet. In anomaly approach most of the behavioural and characteristic identification of the botnet is done by comparing between the normal and anomalous traffic. The main focus of the network analysis is studied on UDP protocol network traffic. Based on the analysis of the network traffic, the following anomalies are identified, anomalous DNS packet request, the NetBIOS attack, anomalous DNS MX query, DNS amplification attack and UDP flood attack. This study, identify significant Botnet characteristic in local network traffic for UDP network as additional approach for Botnet detection mechanism.

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Christiana, Abikoye Oluwakemi, Benjamin Aruwa Gyunka, and Akande Noah. "Android Malware Detection through Machine Learning Techniques: A Review." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 02 (February 12, 2020): 14. http://dx.doi.org/10.3991/ijoe.v16i02.11549.

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The open source nature of Android Operating System has attracted wider adoption of the system by multiple types of developers. This phenomenon has further fostered an exponential proliferation of devices running the Android OS into different sectors of the economy. Although this development has brought about great technological advancements and ease of doing businesses (e-commerce) and social interactions, they have however become strong mediums for the uncontrolled rising cyberattacks and espionage against business infrastructures and the individual users of these mobile devices. Different cyberattacks techniques exist but attacks through malicious applications have taken the lead aside other attack methods like social engineering. Android malware have evolved in sophistications and intelligence that they have become highly resistant to existing detection systems especially those that are signature-based. Machine learning techniques have risen to become a more competent choice for combating the kind of sophistications and novelty deployed by emerging Android malwares. The models created via machine learning methods work by first learning the existing patterns of malware behaviour and then use this knowledge to separate or identify any such similar behaviour from unknown attacks. This paper provided a comprehensive review of machine learning techniques and their applications in Android malware detection as found in contemporary literature.

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Janam, Dr Ram. "Social, Historical and Psychological Realism in Arundhati Roy’s The God of Small Things." SMART MOVES JOURNAL IJELLH 8, no. 5 (May 30, 2020): 70. http://dx.doi.org/10.24113/ijellh.v8i5.10583.

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The God of Small Things depicts realistic picture of the current issues of the typical Indian society. Arundhati Roy has tried her best to cover almost all the details of social and historical setting so that the readers may be able to acquaint with the pattern of living, daily routine, rites, customs, rituals and habits. The book explores how the small things affect people's behaviour and their lives. During that time in India, class was a major issue and still is in many parts of India. Inferiority complex is clearly visible in the interactions between Untouchables and Touchables in Ayemenem. The novel also shows that The Untouchables were considered polluted beings. Betrayal is also a constant theme in this story. Love, ideals, and confidence are all forsaken, consciously and unconsciously, innocently and maliciously, and these deceptions affect all of the characters deeply.

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Faris, Hossam, Maria Habib, Iman Almomani, Mohammed Eshtay, and Ibrahim Aljarah. "Optimizing Extreme Learning Machines Using Chains of Salps for Efficient Android Ransomware Detection." Applied Sciences 10, no. 11 (May 27, 2020): 3706. http://dx.doi.org/10.3390/app10113706.

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Nowadays, smartphones are an essential part of people’s lives and a sign of a contemporary world. Even that smartphones bring numerous facilities, but they form a wide gate into personal and financial information. In recent years, a substantial increasing rate of malicious efforts to attack smartphone vulnerabilities has been noticed. A serious common threat is the ransomware attack, which locks the system or users’ data and demands a ransom for the purpose of decrypting or unlocking them. In this article, a framework based on metaheuristic and machine learning is proposed for the detection of Android ransomware. Raw sequences of the applications API calls and permissions were extracted to capture the ransomware pattern of behaviors and build the detection framework. Then, a hybrid of the Salp Swarm Algorithm (SSA) and Kernel Extreme Learning Machine (KELM) is modeled, where the SSA is used to search for the best subset of features and optimize the KELM hyperparameters. Meanwhile, the KELM algorithm is utilized for the identification and classification of the apps into benign or ransomware. The performance of the proposed (SSA-KELM) exhibits noteworthy advantages based on several evaluation measures, including accuracy, recall, true negative rate, precision, g-mean, and area under the curve of a value of 98%, and a ratio of 2% of false positive rate. In addition, it has a competitive convergence ability. Hence, the proposed SSA-KELM algorithm represents a promising approach for efficient ransomware detection.

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Tan, Rong, Yuan Tao, Wen Si, and Yuan-Yuan Zhang. "Privacy preserving semantic trajectory data publishing for mobile location-based services." Wireless Networks 26, no. 8 (June 15, 2019): 5551–60. http://dx.doi.org/10.1007/s11276-019-02058-8.

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Abstract The development of wireless technologies and the popularity of mobile devices is responsible for generating large amounts of trajectory data for moving objects. Trajectory datasets have spatiotemporal features and are a rich information source. The mining of trajectory data can reveal interesting patterns of human activities and behaviors. However, trajectory data can also be exploited to disclose users’ privacy information, e.g., the places they live and work, which could be abused by a malicious user. Therefore, it is very important to protect the users’ privacy before publishing any trajectory data. While most previous research on this subject has only considered the privacy protection of stay points, this paper distinguishes itself by modeling and processing semantic trajectories, which not only contain spatiotemporal data but also involve POI information and the users’ motion modes such as walking, running, driving, etc. Accordingly, in this research, semantic trajectory anonymizing based on the k-anonymity model is proposed that can form sensitive areas that contain k − 1 POI points that are similar to the sensitive points. Then, trajectory ambiguity is executed based on the motion modes, road network topologies and road weights in the sensitive area. Finally, a similarity comparison is performed to obtain the recordable and releasable anonymity trajectory sets. Experimental results show that this method performs efficiently and provides high privacy levels.

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Hu, Jinlong, Tenghui Li, Yi Zhuang, Song Huang, and Shoubin Dong. "GFD: A Weighted Heterogeneous Graph Embedding Based Approach for Fraud Detection in Mobile Advertising." Security and Communication Networks 2020 (September 4, 2020): 1–12. http://dx.doi.org/10.1155/2020/8810817.

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Online mobile advertising plays a vital role in the mobile app ecosystem. The mobile advertising frauds caused by fraudulent clicks or other actions on advertisements are considered one of the most critical issues in mobile advertising systems. To combat the evolving mobile advertising frauds, machine learning methods have been successfully applied to identify advertising frauds in tabular data, distinguishing suspicious advertising fraud operation from normal one. However, such approaches may suffer from labor-intensive feature engineering and robustness of the detection algorithms, since the online advertising big data and complex fraudulent advertising actions generated by malicious codes, botnets, and click-firms are constantly changing. In this paper, we propose a novel weighted heterogeneous graph embedding and deep learning-based fraud detection approach, namely, GFD, to identify fraudulent apps for mobile advertising. In the proposed GFD approach, (i) we construct a weighted heterogeneous graph to represent behavior patterns between users, mobile apps, and mobile ads and design a weighted metapath to vector algorithm to learn node representations (graph-based features) from the graph; (ii) we use a time window based statistical analysis method to extract intrinsic features (attribute-based features) from the tabular sample data; (iii) we propose a hybrid neural network to fuse graph-based features and attribute-based features for classifying the fraudulent apps from normal apps. The GFD approach was applied on a large real-world mobile advertising dataset, and experiment results demonstrate that the approach significantly outperforms well-known learning methods.

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Marchenko, Konstantyn, Oleh Oryshaka, and Anzhelyka Marchenko. "Information Security Challenges in the Context of the Epidemic." Central Ukrainian Scientific Bulletin. Technical Sciences, no. 3(34) (October 2020): 22–31. http://dx.doi.org/10.32515/2664-262x.2020.3(34).22-31.

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The article reviewed the informational causes of diseases and the peculiarities of the influence of the mass media on human consciousness during epidemics. The aim of the research is to study the patterns of the impact of information on the human condition during epidemics and to develop safety measures when interacting with information. The impact of information on people during epidemics is increasing. The media are the main sources of information for the general consumer. Analysis of the media supplied shows that the media is destructive. The pressure on the end-user can be indirectly described by the number of messages per topic relative to the size of the news sample. Everyone has their own unique information system, In case of manipulative manipulation of the consumer, the information is prepared in order to penetrate the mind. Malicious information introduced into the mind is a Trojan virus, a Trojan program designed to change the programs that operate in the information system of the addressee. An unprotected mind is both a portal for the introduction of artificial information and a key tool for the realization of the manipulator’s goals and plans. Information viruses affect the workings of human psychic programs, which are used to deliberately reprogram human behavior through suggestion, zombie. The content of the human information system affects both health and quality of life. As the administrator of your own information system, a person needs to install network filters with rules for distinguishing between data and programs, restrict access to incoming data and access to their software. Based on the proposed approach, the following recommendations can be made to the consumer of the information: to assume the role of administrator of their information system, to be responsible for its state of affairs; filter incoming information for usability, verify data before use; respect the principle of constructive information. Information should be useful, help to solve problems, empower people and defuse tensions; avoid redundancy, information overload when the quality of filtration and security is reduced; carry out continuous background scanning and regular cleansing of its information system, identifying redundant, false information and destructive behaviour programmes; use a channel with an individual unique frequency for information exchange.

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Huancayo Ramos, Katherinne Shirley, Marco Antonio Sotelo Monge, and Jorge Maestre Vidal. "Benchmark-Based Reference Model for Evaluating Botnet Detection Tools Driven by Traffic-Flow Analytics." Sensors 20, no. 16 (August 12, 2020): 4501. http://dx.doi.org/10.3390/s20164501.

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Botnets are some of the most recurrent cyber-threats, which take advantage of the wide heterogeneity of endpoint devices at the Edge of the emerging communication environments for enabling the malicious enforcement of fraud and other adversarial tactics, including malware, data leaks or denial of service. There have been significant research advances in the development of accurate botnet detection methods underpinned on supervised analysis but assessing the accuracy and performance of such detection methods requires a clear evaluation model in the pursuit of enforcing proper defensive strategies. In order to contribute to the mitigation of botnets, this paper introduces a novel evaluation scheme grounded on supervised machine learning algorithms that enable the detection and discrimination of different botnets families on real operational environments. The proposal relies on observing, understanding and inferring the behavior of each botnet family based on network indicators measured at flow-level. The assumed evaluation methodology contemplates six phases that allow building a detection model against botnet-related malware distributed through the network, for which five supervised classifiers were instantiated were instantiated for further comparisons—Decision Tree, Random Forest, Naive Bayes Gaussian, Support Vector Machine and K-Neighbors. The experimental validation was performed on two public datasets of real botnet traffic—CIC-AWS-2018 and ISOT HTTP Botnet. Bearing the heterogeneity of the datasets, optimizing the analysis with the Grid Search algorithm led to improve the classification results of the instantiated algorithms. An exhaustive evaluation was carried out demonstrating the adequateness of our proposal which prompted that Random Forest and Decision Tree models are the most suitable for detecting different botnet specimens among the chosen algorithms. They exhibited higher precision rates whilst analyzing a large number of samples with less processing time. The variety of testing scenarios were deeply assessed and reported to set baseline results for future benchmark analysis targeted on flow-based behavioral patterns.

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Jeong, Sihyun, and Kyu-haeng Lee. "Spam Classification Based on Signed Network Analysis." Applied Sciences 10, no. 24 (December 15, 2020): 8952. http://dx.doi.org/10.3390/app10248952.

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Online social networking services have become the most important information-sharing medium of modern society due to several merits, such as creating opportunities to broaden social relations, easy and instant communication, and fast data propagation. These advantages, however, are being abused by malicious users to disseminate unsolicited spam messages, causing great harm to both users and service providers. To address this problem, numerous spam detection methods utilizing various spam characteristics have been proposed, but most of them suffer from several limitations. Using individual behaviors and the content of messages for spam classification has been revealed to have bounded performance, since attackers can easily fake them. Instead, exploitation of social-network-related features has been highlighted as an alternative solution, but recent spam attacks can adroitly avoid these methods by controlling their ranking through various forms of attack. In this paper, we delineate a signed-network-analysis-based spam classification method. Our key hypothesis is that the edge signs are highly likely to be determined by considering users’ social relationships, so there will be a substantial difference between the edge sign patterns of spammers and that of non-spammers. To identify our hypothesis, we employ two social psychological theories for signed networks—structural balance theory and social status theory—and the concept of surprise is adopted to quantitatively analyze the given network according to these theories. These surprise measurements are then used as the main features for spam classification. In addition, we develop a graph-converting method for applying our scheme to unsigned networks. Extensive experimental results with Twitter and Epinions datasets show that the proposed scheme obtains significant classification performance improvement compared to conventional schemes.

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Krishna, T. Shiva Rama. "Malware Detection using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1847–53. http://dx.doi.org/10.22214/ijraset.2021.35426.

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Malicious software or malware continues to pose a major security concern in this digital age as computer users, corporations, and governments witness an exponential growth in malware attacks. Current malware detection solutions adopt Static and Dynamic analysis of malware signatures and behaviour patterns that are time consuming and ineffective in identifying unknown malwares. Recent malwares use polymorphic, metamorphic and other evasive techniques to change the malware behaviour’s quickly and to generate large number of malwares. Since new malwares are predominantly variants of existing malwares, machine learning algorithms are being employed recently to conduct an effective malware analysis. This requires extensive feature engineering, feature learning and feature representation. By using the advanced MLAs such as deep learning, the feature engineering phase can be completely avoided. Though some recent research studies exist in this direction, the performance of the algorithms is biased with the training data. There is a need to mitigate bias and evaluate these methods independently in order to arrive at new enhanced methods for effective zero-day malware detection. To fill the gap in literature, this work evaluates classical MLAs and deep learning architectures for malware detection, classification and categorization with both public and private datasets. The train and test splits of public and private datasets used in the experimental analysis are disjoint to each other’s and collected in different timescales. In addition, we propose a novel image processing technique with optimal parameters for MLAs and deep learning architectures. A comprehensive experimental evaluation of these methods indicate that deep learning architectures outperform classical MLAs. Overall, this work proposes an effective visual detection of malware using a scalable and hybrid deep learning framework for real-time deployments. The visualization and deep learning architectures for static, dynamic and image processing-based hybrid approach in a big data environment is a new enhanced method for effective zero-day malware detection.

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Nayyar, Anand, Pijush Kanti Dutta Pramankit, and Rajni Mohana. "Introduction to the Special Issue on Evolving IoT and Cyber-Physical Systems: Advancements, Applications, and Solutions." Scalable Computing: Practice and Experience 21, no. 3 (August 1, 2020): 347–48. http://dx.doi.org/10.12694/scpe.v21i3.1568.

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Internet of Things (IoT) is regarded as a next-generation wave of Information Technology (IT) after the widespread emergence of the Internet and mobile communication technologies. IoT supports information exchange and networked interaction of appliances, vehicles and other objects, making sensing and actuation possible in a low-cost and smart manner. On the other hand, cyber-physical systems (CPS) are described as the engineered systems which are built upon the tight integration of the cyber entities (e.g., computation, communication, and control) and the physical things (natural and man-made systems governed by the laws of physics). The IoT and CPS are not isolated technologies. Rather it can be said that IoT is the base or enabling technology for CPS and CPS is considered as the grownup development of IoT, completing the IoT notion and vision. Both are merged into closed-loop, providing mechanisms for conceptualizing, and realizing all aspects of the networked composed systems that are monitored and controlled by computing algorithms and are tightly coupled among users and the Internet. That is, the hardware and the software entities are intertwined, and they typically function on different time and location-based scales. In fact, the linking between the cyber and the physical world is enabled by IoT (through sensors and actuators). CPS that includes traditional embedded and control systems are supposed to be transformed by the evolving and innovative methodologies and engineering of IoT. Several applications areas of IoT and CPS are smart building, smart transport, automated vehicles, smart cities, smart grid, smart manufacturing, smart agriculture, smart healthcare, smart supply chain and logistics, etc. Though CPS and IoT have significant overlaps, they differ in terms of engineering aspects. Engineering IoT systems revolves around the uniquely identifiable and internet-connected devices and embedded systems; whereas engineering CPS requires a strong emphasis on the relationship between computation aspects (complex software) and the physical entities (hardware). Engineering CPS is challenging because there is no defined and fixed boundary and relationship between the cyber and physical worlds. In CPS, diverse constituent parts are composed and collaborated together to create unified systems with global behaviour. These systems need to be ensured in terms of dependability, safety, security, efficiency, and adherence to real‐time constraints. Hence, designing CPS requires knowledge of multidisciplinary areas such as sensing technologies, distributed systems, pervasive and ubiquitous computing, real-time computing, computer networking, control theory, signal processing, embedded systems, etc. CPS, along with the continuous evolving IoT, has posed several challenges. For example, the enormous amount of data collected from the physical things makes it difficult for Big Data management and analytics that includes data normalization, data aggregation, data mining, pattern extraction and information visualization. Similarly, the future IoT and CPS need standardized abstraction and architecture that will allow modular designing and engineering of IoT and CPS in global and synergetic applications. Another challenging concern of IoT and CPS is the security and reliability of the components and systems. Although IoT and CPS have attracted the attention of the research communities and several ideas and solutions are proposed, there are still huge possibilities for innovative propositions to make IoT and CPS vision successful. The major challenges and research scopes include system design and implementation, computing and communication, system architecture and integration, application-based implementations, fault tolerance, designing efficient algorithms and protocols, availability and reliability, security and privacy, energy-efficiency and sustainability, etc. It is our great privilege to present Volume 21, Issue 3 of Scalable Computing: Practice and Experience. We had received 30 research papers and out of which 14 papers are selected for publication. The objective of this special issue is to explore and report recent advances and disseminate state-of-the-art research related to IoT, CPS and the enabling and associated technologies. The special issue will present new dimensions of research to researchers and industry professionals with regard to IoT and CPS. Vivek Kumar Prasad and Madhuri D Bhavsar in the paper titled "Monitoring and Prediction of SLA for IoT based Cloud described the mechanisms for monitoring by using the concept of reinforcement learning and prediction of the cloud resources, which forms the critical parts of cloud expertise in support of controlling and evolution of the IT resources and has been implemented using LSTM. The proper utilization of the resources will generate revenues to the provider and also increases the trust factor of the provider of cloud services. For experimental analysis, four parameters have been used i.e. CPU utilization, disk read/write throughput and memory utilization. Kasture et al. in the paper titled "Comparative Study of Speaker Recognition Techniques in IoT Devices for Text Independent Negative Recognition" compared the performance of features which are used in state of art speaker recognition models and analyse variants of Mel frequency cepstrum coefficients (MFCC) predominantly used in feature extraction which can be further incorporated and used in various smart devices. Mahesh Kumar Singh and Om Prakash Rishi in the paper titled "Event Driven Recommendation System for E-Commerce using Knowledge based Collaborative Filtering Technique" proposed a novel system that uses a knowledge base generated from knowledge graph to identify the domain knowledge of users, items, and relationships among these, knowledge graph is a labelled multidimensional directed graph that represents the relationship among the users and the items. The proposed approach uses about 100 percent of users' participation in the form of activities during navigation of the web site. Thus, the system expects under the users' interest that is beneficial for both seller and buyer. The proposed system is compared with baseline methods in area of recommendation system using three parameters: precision, recall and NDGA through online and offline evaluation studies with user data and it is observed that proposed system is better as compared to other baseline systems. Benbrahim et al. in the paper titled "Deep Convolutional Neural Network with TensorFlow and Keras to Classify Skin Cancer" proposed a novel classification model to classify skin tumours in images using Deep Learning methodology and the proposed system was tested on HAM10000 dataset comprising of 10,015 dermatoscopic images and the results observed that the proposed system is accurate in order of 94.06\% in validation set and 93.93\% in the test set. Devi B et al. in the paper titled "Deadlock Free Resource Management Technique for IoT-Based Post Disaster Recovery Systems" proposed a new class of techniques that do not perform stringent testing before allocating the resources but still ensure that the system is deadlock-free and the overhead is also minimal. The proposed technique suggests reserving a portion of the resources to ensure no deadlock would occur. The correctness of the technique is proved in the form of theorems. The average turnaround time is approximately 18\% lower for the proposed technique over Banker's algorithm and also an optimal overhead of O(m). Deep et al. in the paper titled "Access Management of User and Cyber-Physical Device in DBAAS According to Indian IT Laws Using Blockchain" proposed a novel blockchain solution to track the activities of employees managing cloud. Employee authentication and authorization are managed through the blockchain server. User authentication related data is stored in blockchain. The proposed work assists cloud companies to have better control over their employee's activities, thus help in preventing insider attack on User and Cyber-Physical Devices. Sumit Kumar and Jaspreet Singh in paper titled "Internet of Vehicles (IoV) over VANETS: Smart and Secure Communication using IoT" highlighted a detailed description of Internet of Vehicles (IoV) with current applications, architectures, communication technologies, routing protocols and different issues. The researchers also elaborated research challenges and trade-off between security and privacy in area of IoV. Deore et al. in the paper titled "A New Approach for Navigation and Traffic Signs Indication Using Map Integrated Augmented Reality for Self-Driving Cars" proposed a new approach to supplement the technology used in self-driving cards for perception. The proposed approach uses Augmented Reality to create and augment artificial objects of navigational signs and traffic signals based on vehicles location to reality. This approach help navigate the vehicle even if the road infrastructure does not have very good sign indications and marking. The approach was tested locally by creating a local navigational system and a smartphone based augmented reality app. The approach performed better than the conventional method as the objects were clearer in the frame which made it each for the object detection to detect them. Bhardwaj et al. in the paper titled "A Framework to Systematically Analyse the Trustworthiness of Nodes for Securing IoV Interactions" performed literature on IoV and Trust and proposed a Hybrid Trust model that seperates the malicious and trusted nodes to secure the interaction of vehicle in IoV. To test the model, simulation was conducted on varied threshold values. And results observed that PDR of trusted node is 0.63 which is higher as compared to PDR of malicious node which is 0.15. And on the basis of PDR, number of available hops and Trust Dynamics the malicious nodes are identified and discarded. Saniya Zahoor and Roohie Naaz Mir in the paper titled "A Parallelization Based Data Management Framework for Pervasive IoT Applications" highlighted the recent studies and related information in data management for pervasive IoT applications having limited resources. The paper also proposes a parallelization-based data management framework for resource-constrained pervasive applications of IoT. The comparison of the proposed framework is done with the sequential approach through simulations and empirical data analysis. The results show an improvement in energy, processing, and storage requirements for the processing of data on the IoT device in the proposed framework as compared to the sequential approach. Patel et al. in the paper titled "Performance Analysis of Video ON-Demand and Live Video Streaming Using Cloud Based Services" presented a review of video analysis over the LVS \& VoDS video application. The researchers compared different messaging brokers which helps to deliver each frame in a distributed pipeline to analyze the impact on two message brokers for video analysis to achieve LVS & VoS using AWS elemental services. In addition, the researchers also analysed the Kafka configuration parameter for reliability on full-service-mode. Saniya Zahoor and Roohie Naaz Mir in the paper titled "Design and Modeling of Resource-Constrained IoT Based Body Area Networks" presented the design and modeling of a resource-constrained BAN System and also discussed the various scenarios of BAN in context of resource constraints. The Researchers also proposed an Advanced Edge Clustering (AEC) approach to manage the resources such as energy, storage, and processing of BAN devices while performing real-time data capture of critical health parameters and detection of abnormal patterns. The comparison of the AEC approach is done with the Stable Election Protocol (SEP) through simulations and empirical data analysis. The results show an improvement in energy, processing time and storage requirements for the processing of data on BAN devices in AEC as compared to SEP. Neelam Saleem Khan and Mohammad Ahsan Chishti in the paper titled "Security Challenges in Fog and IoT, Blockchain Technology and Cell Tree Solutions: A Review" outlined major authentication issues in IoT, map their existing solutions and further tabulate Fog and IoT security loopholes. Furthermore, this paper presents Blockchain, a decentralized distributed technology as one of the solutions for authentication issues in IoT. In addition, the researchers discussed the strength of Blockchain technology, work done in this field, its adoption in COVID-19 fight and tabulate various challenges in Blockchain technology. The researchers also proposed Cell Tree architecture as another solution to address some of the security issues in IoT, outlined its advantages over Blockchain technology and tabulated some future course to stir some attempts in this area. Bhadwal et al. in the paper titled "A Machine Translation System from Hindi to Sanskrit Language Using Rule Based Approach" proposed a rule-based machine translation system to bridge the language barrier between Hindi and Sanskrit Language by converting any test in Hindi to Sanskrit. The results are produced in the form of two confusion matrices wherein a total of 50 random sentences and 100 tokens (Hindi words or phrases) were taken for system evaluation. The semantic evaluation of 100 tokens produce an accuracy of 94\% while the pragmatic analysis of 50 sentences produce an accuracy of around 86\%. Hence, the proposed system can be used to understand the whole translation process and can further be employed as a tool for learning as well as teaching. Further, this application can be embedded in local communication based assisting Internet of Things (IoT) devices like Alexa or Google Assistant. Anshu Kumar Dwivedi and A.K. Sharma in the paper titled "NEEF: A Novel Energy Efficient Fuzzy Logic Based Clustering Protocol for Wireless Sensor Network" proposed a a deterministic novel energy efficient fuzzy logic-based clustering protocol (NEEF) which considers primary and secondary factors in fuzzy logic system while selecting cluster heads. After selection of cluster heads, non-cluster head nodes use fuzzy logic for prudent selection of their cluster head for cluster formation. NEEF is simulated and compared with two recent state of the art protocols, namely SCHFTL and DFCR under two scenarios. Simulation results unveil better performance by balancing the load and improvement in terms of stability period, packets forwarded to the base station, improved average energy and extended lifetime.

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Abdelhedi, Fatma, and Nabil Derbel. "Volume 2, Issue 3, Special issue on Recent Advances in Engineering Systems (Published Papers) Articles Transmit / Received Beamforming for Frequency Diverse Array with Symmetrical frequency offsets Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. J. 2(3), 1-6 (2017); View Description Detailed Analysis of Amplitude and Slope Diffraction Coefficients for knife-edge structure in S-UTD-CH Model Eray Arik, Mehmet Baris Tabakcioglu Adv. Sci. Technol. Eng. Syst. J. 2(3), 7-11 (2017); View Description Applications of Case Based Organizational Memory Supported by the PAbMM Architecture Martín, María de los Ángeles, Diván, Mario José Adv. Sci. Technol. Eng. Syst. J. 2(3), 12-23 (2017); View Description Low Probability of Interception Beampattern Using Frequency Diverse Array Antenna Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. J. 2(3), 24-29 (2017); View Description Zero Trust Cloud Networks using Transport Access Control and High Availability Optical Bypass Switching Casimer DeCusatis, Piradon Liengtiraphan, Anthony Sager Adv. Sci. Technol. Eng. Syst. J. 2(3), 30-35 (2017); View Description A Derived Metrics as a Measurement to Support Efficient Requirements Analysis and Release Management Indranil Nath Adv. Sci. Technol. Eng. Syst. J. 2(3), 36-40 (2017); View Description Feedback device of temperature sensation for a myoelectric prosthetic hand Yuki Ueda, Chiharu Ishii Adv. Sci. Technol. Eng. Syst. J. 2(3), 41-40 (2017); View Description Deep venous thrombus characterization: ultrasonography, elastography and scattering operator Thibaud Berthomier, Ali Mansour, Luc Bressollette, Frédéric Le Roy, Dominique Mottier Adv. Sci. Technol. Eng. Syst. J. 2(3), 48-59 (2017); View Description Improving customs’ border control by creating a reference database of cargo inspection X-ray images Selina Kolokytha, Alexander Flisch, Thomas Lüthi, Mathieu Plamondon, Adrian Schwaninger, Wicher Vasser, Diana Hardmeier, Marius Costin, Caroline Vienne, Frank Sukowski, Ulf Hassler, Irène Dorion, Najib Gadi, Serge Maitrejean, Abraham Marciano, Andrea Canonica, Eric Rochat, Ger Koomen, Micha Slegt Adv. Sci. Technol. Eng. Syst. J. 2(3), 60-66 (2017); View Description Aviation Navigation with Use of Polarimetric Technologies Arsen Klochan, Ali Al-Ammouri, Viktor Romanenko, Vladimir Tronko Adv. Sci. Technol. Eng. Syst. J. 2(3), 67-72 (2017); View Description Optimization of Multi-standard Transmitter Architecture Using Single-Double Conversion Technique Used for Rescue Operations Riadh Essaadali, Said Aliouane, Chokri Jebali and Ammar Kouki Adv. Sci. Technol. Eng. Syst. J. 2(3), 73-81 (2017); View Description Singular Integral Equations in Electromagnetic Waves Reflection Modeling A. S. Ilinskiy, T. N. Galishnikova Adv. Sci. Technol. Eng. Syst. J. 2(3), 82-87 (2017); View Description Methodology for Management of Information Security in Industrial Control Systems: A Proof of Concept aligned with Enterprise Objectives. Fabian Bustamante, Walter Fuertes, Paul Diaz, Theofilos Toulqueridis Adv. Sci. Technol. Eng. Syst. J. 2(3), 88-99 (2017); View Description Dependence-Based Segmentation Approach for Detecting Morpheme Boundaries Ahmed Khorsi, Abeer Alsheddi Adv. Sci. Technol. Eng. Syst. J. 2(3), 100-110 (2017); View Description Paper Improving Rule Based Stemmers to Solve Some Special Cases of Arabic Language Soufiane Farrah, Hanane El Manssouri, Ziyati Elhoussaine, Mohamed Ouzzif Adv. Sci. Technol. Eng. Syst. J. 2(3), 111-115 (2017); View Description Medical imbalanced data classification Sara Belarouci, Mohammed Amine Chikh Adv. Sci. Technol. Eng. Syst. J. 2(3), 116-124 (2017); View Description ADOxx Modelling Method Conceptualization Environment Nesat Efendioglu, Robert Woitsch, Wilfrid Utz, Damiano Falcioni Adv. Sci. Technol. Eng. Syst. J. 2(3), 125-136 (2017); View Description GPSR+Predict: An Enhancement for GPSR to Make Smart Routing Decision by Anticipating Movement of Vehicles in VANETs Zineb Squalli Houssaini, Imane Zaimi, Mohammed Oumsis, Saïd El Alaoui Ouatik Adv. Sci. Technol. Eng. Syst. J. 2(3), 137-146 (2017); View Description Optimal Synthesis of Universal Space Vector Digital Algorithm for Matrix Converters Adrian Popovici, Mircea Băbăiţă, Petru Papazian Adv. Sci. Technol. Eng. Syst. J. 2(3), 147-152 (2017); View Description Control design for axial flux permanent magnet synchronous motor which operates above the nominal speed Xuan Minh Tran, Nhu Hien Nguyen, Quoc Tuan Duong Adv. Sci. Technol. Eng. Syst. J. 2(3), 153-159 (2017); View Description A synchronizing second order sliding mode control applied to decentralized time delayed multi−agent robotic systems: Stability Proof Marwa Fathallah, Fatma Abdelhedi, Nabil Derbel Adv. Sci. Technol. Eng. Syst. J. 2(3), 160-170 (2017); View Description Fault Diagnosis and Tolerant Control Using Observer Banks Applied to Continuous Stirred Tank Reactor Martin F. Pico, Eduardo J. Adam Adv. Sci. Technol. Eng. Syst. J. 2(3), 171-181 (2017); View Description Development and Validation of a Heat Pump System Model Using Artificial Neural Network Nabil Nassif, Jordan Gooden Adv. Sci. Technol. Eng. Syst. J. 2(3), 182-185 (2017); View Description Assessment of the usefulness and appeal of stigma-stop by psychology students: a serious game designed to reduce the stigma of mental illness Adolfo J. Cangas, Noelia Navarro, Juan J. Ojeda, Diego Cangas, Jose A. Piedra, José Gallego Adv. Sci. Technol. Eng. Syst. J. 2(3), 186-190 (2017); View Description Kinect-Based Moving Human Tracking System with Obstacle Avoidance Abdel Mehsen Ahmad, Zouhair Bazzal, Hiba Al Youssef Adv. Sci. Technol. Eng. Syst. J. 2(3), 191-197 (2017); View Description A security approach based on honeypots: Protecting Online Social network from malicious profiles Fatna Elmendili, Nisrine Maqran, Younes El Bouzekri El Idrissi, Habiba Chaoui Adv. Sci. Technol. Eng. Syst. J. 2(3), 198-204 (2017); View Description Pulse Generator for Ultrasonic Piezoelectric Transducer Arrays Based on a Programmable System-on-Chip (PSoC) Pedro Acevedo, Martín Fuentes, Joel Durán, Mónica Vázquez, Carlos Díaz Adv. Sci. Technol. Eng. Syst. J. 2(3), 205-209 (2017); View Description Enabling Toy Vehicles Interaction With Visible Light Communication (VLC) M. A. Ilyas, M. B. Othman, S. M. Shah, Mas Fawzi Adv. Sci. Technol. Eng. Syst. J. 2(3), 210-216 (2017); View Description Analysis of Fractional-Order 2xn RLC Networks by Transmission Matrices Mahmut Ün, Manolya Ün Adv. Sci. Technol. Eng. Syst. J. 2(3), 217-220 (2017); View Description Fire extinguishing system in large underground garages Ivan Antonov, Rositsa Velichkova, Svetlin Antonov, Kamen Grozdanov, Milka Uzunova, Ikram El Abbassi Adv. Sci. Technol. Eng. Syst. J. 2(3), 221-226 (2017); View Description Directional Antenna Modulation Technique using A Two-Element Frequency Diverse Array Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. J. 2(3), 227-232 (2017); View Description Classifying region of interests from mammograms with breast cancer into BIRADS using Artificial Neural Networks Estefanía D. Avalos-Rivera, Alberto de J. Pastrana-Palma Adv. Sci. Technol. Eng. Syst. J. 2(3), 233-240 (2017); View Description Magnetically Levitated and Guided Systems Florian Puci, Miroslav Husak Adv. Sci. Technol. Eng. Syst. J. 2(3), 241-244 (2017); View Description Energy-Efficient Mobile Sensing in Distributed Multi-Agent Sensor Networks Minh T. Nguyen Adv. Sci. Technol. Eng. Syst. J. 2(3), 245-253 (2017); View Description Validity and efficiency of conformal anomaly detection on big distributed data Ilia Nouretdinov Adv. Sci. Technol. Eng. Syst. J. 2(3), 254-267 (2017); View Description S-Parameters Optimization in both Segmented and Unsegmented Insulated TSV upto 40GHz Frequency Juma Mary Atieno, Xuliang Zhang, HE Song Bai Adv. Sci. Technol. Eng. Syst. J. 2(3), 268-276 (2017); View Description Synthesis of Important Design Criteria for Future Vehicle Electric System Lisa Braun, Eric Sax Adv. Sci. Technol. Eng. Syst. J. 2(3), 277-283 (2017); View Description Gestural Interaction for Virtual Reality Environments through Data Gloves G. Rodriguez, N. Jofre, Y. Alvarado, J. Fernández, R. Guerrero Adv. Sci. Technol. Eng. Syst. J. 2(3), 284-290 (2017); View Description Solving the Capacitated Network Design Problem in Two Steps Meriem Khelifi, Mohand Yazid Saidi, Saadi Boudjit Adv. Sci. Technol. Eng. Syst. J. 2(3), 291-301 (2017); View Description A Computationally Intelligent Approach to the Detection of Wormhole Attacks in Wireless Sensor Networks Mohammad Nurul Afsar Shaon, Ken Ferens Adv. Sci. Technol. Eng. Syst. J. 2(3), 302-320 (2017); View Description Real Time Advanced Clustering System Giuseppe Spampinato, Arcangelo Ranieri Bruna, Salvatore Curti, Viviana D’Alto Adv. Sci. Technol. Eng. Syst. J. 2(3), 321-326 (2017); View Description Indoor Mobile Robot Navigation in Unknown Environment Using Fuzzy Logic Based Behaviors Khalid Al-Mutib, Foudil Abdessemed Adv. Sci. Technol. Eng. Syst. J. 2(3), 327-337 (2017); View Description Validity of Mind Monitoring System as a Mental Health Indicator using Voice Naoki Hagiwara, Yasuhiro Omiya, Shuji Shinohara, Mitsuteru Nakamura, Masakazu Higuchi, Shunji Mitsuyoshi, Hideo Yasunaga, Shinichi Tokuno Adv. Sci. Technol. Eng. Syst. J. 2(3), 338-344 (2017); View Description The Model of Adaptive Learning Objects for virtual environments instanced by the competencies Carlos Guevara, Jose Aguilar, Alexandra González-Eras Adv. Sci. Technol. Eng. Syst. J. 2(3), 345-355 (2017); View Description An Overview of Traceability: Towards a general multi-domain model Kamal Souali, Othmane Rahmaoui, Mohammed Ouzzif Adv. Sci. Technol. Eng. Syst. J. 2(3), 356-361 (2017); View Description L-Band SiGe HBT Active Differential Equalizers with Variable, Positive or Negative Gain Slopes Using Dual-Resonant RLC Circuits Yasushi Itoh, Hiroaki Takagi Adv. Sci. Technol. Eng. Syst. J. 2(3), 362-368 (2017); View Description Moving Towards Reliability-Centred Management of Energy, Power and Transportation Assets Kang Seng Seow, Loc K. Nguyen, Kelvin Tan, Kees-Jan Van Oeveren Adv. Sci. Technol. Eng. Syst. J. 2(3), 369-375 (2017); View Description Secure Path Selection under Random Fading Furqan Jameel, Faisal, M Asif Ali Haider, Amir Aziz Butt Adv. Sci. Technol. Eng. Syst. J. 2(3), 376-383 (2017); View Description Security in SWIPT with Power Splitting Eavesdropper Furqan Jameel, Faisal, M Asif Ali Haider, Amir Aziz Butt Adv. Sci. Technol. Eng. Syst. J. 2(3), 384-388 (2017); View Description Performance Analysis of Phased Array and Frequency Diverse Array Radar Ambiguity Functions Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. J. 2(3), 389-394 (2017); View Description Adaptive Discrete-time Fuzzy Sliding Mode Control For a Class of Chaotic Systems Hanene Medhaffar, Moez Feki, Nabil Derbel Adv. Sci. Technol. Eng. Syst. J. 2(3), 395-400 (2017); View Description Fault Tolerant Inverter Topology for the Sustainable Drive of an Electrical Helicopter Igor Bolvashenkov, Jörg Kammermann, Taha Lahlou, Hans-Georg Herzog Adv. Sci. Technol. Eng. Syst. J. 2(3), 401-411 (2017); View Description Computational Intelligence Methods for Identifying Voltage Sag in Smart Grid Turgay Yalcin, Muammer Ozdemir Adv. Sci. Technol. Eng. Syst. J. 2(3), 412-419 (2017); View Description A Highly-Secured Arithmetic Hiding cum Look-Up Table (AHLUT) based S-Box for AES-128 Implementation Ali Akbar Pammu, Kwen-Siong Chong, Bah-Hwee Gwee Adv. Sci. Technol. Eng. Syst. J. 2(3), 420-426 (2017); View Description Service Productivity and Complexity in Medical Rescue Services Markus Harlacher, Andreas Petz, Philipp Przybysz, Olivia Chaillié, Susanne Mütze-Niewöhner Adv. Sci. Technol. Eng. Syst. J. 2(3), 427-434 (2017); View Description Principal Component Analysis Application on Flavonoids Characterization Che Hafizah Che Noh, Nor Fadhillah Mohamed Azmin, Azura Amid Adv. Sci. Technol. Eng. Syst. J. 2(3), 435-440 (2017); View Description A Reconfigurable Metal-Plasma Yagi-Yuda Antenna for Microwave Applications Giulia Mansutti, Davide Melazzi, Antonio-Daniele Capobianco Adv. Sci. Technol. Eng. Syst. J. 2(3), 441-448 (2017); View Description Verifying the Detection Results of Impersonation Attacks in Service Clouds Sarra Alqahtani, Rose Gamble Adv. Sci. Technol. Eng. Syst. J. 2(3), 449-459 (2017); View Description Image Segmentation Using Fuzzy Inference System on YCbCr Color Model Alvaro Anzueto-Rios, Jose Antonio Moreno-Cadenas, Felipe Gómez-Castañeda, Sergio Garduza-Gonzalez Adv. Sci. Technol. Eng. Syst. J. 2(3), 460-468 (2017); View Description Segmented and Detailed Visualization of Anatomical Structures based on Augmented Reality for Health Education and Knowledge Discovery Isabel Cristina Siqueira da Silva, Gerson Klein, Denise Munchen Brandão Adv. Sci. Technol. Eng. Syst. J. 2(3), 469-478 (2017); View Description Intrusion detection in cloud computing based attack patterns and risk assessment Ben Charhi Youssef, Mannane Nada, Bendriss Elmehdi, Regragui Boubker Adv. Sci. Technol. Eng. Syst. J. 2(3), 479-484 (2017); View Description Optimal Sizing and Control Strategy of renewable hybrid systems PV-Diesel Generator-Battery: application to the case of Djanet city of Algeria Adel Yahiaoui, Khelifa Benmansour, Mohamed Tadjine Adv. Sci. Technol. Eng. Syst. J. 2(3), 485-491 (2017); View Description RFID Antenna Near-field Characterization Using a New 3D Magnetic Field Probe Kassem Jomaa, Fabien Ndagijimana, Hussam Ayad, Majida Fadlallah, Jalal Jomaah Adv. Sci. Technol. Eng. Syst. J. 2(3), 492-497 (2017); View Description Design, Fabrication and Testing of a Dual-Range XY Micro-Motion Stage Driven by Voice Coil Actuators Xavier Herpe, Matthew Dunnigan, Xianwen Kong Adv. Sci. Technol. Eng. Syst. J. 2(3), 498-504 (2017); View Description Self-Organizing Map based Feature Learning in Bio-Signal Processing Marwa Farouk Ibrahim Ibrahim, Adel Ali Al-Jumaily Adv. Sci. Technol. Eng. Syst. J. 2(3), 505-512 (2017); View Description A delay-dependent distributed SMC for stabilization of a networked robotic system exposed to external disturbances." Advances in Science, Technology and Engineering Systems Journal 2, no. 3 (June 2016): 513–19. http://dx.doi.org/10.25046/aj020366.

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Biran, Yahav, George Collins, Borky John M, and Joel Dubow. "Volume 2, Issue 3, Special issue on Recent Advances in Engineering Systems (Published Papers) Articles Transmit / Received Beamforming for Frequency Diverse Array with Symmetrical frequency offsets Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. J. 2(3), 1-6 (2017); View Description Detailed Analysis of Amplitude and Slope Diffraction Coefficients for knife-edge structure in S-UTD-CH Model Eray Arik, Mehmet Baris Tabakcioglu Adv. Sci. Technol. Eng. Syst. J. 2(3), 7-11 (2017); View Description Applications of Case Based Organizational Memory Supported by the PAbMM Architecture Martín, María de los Ángeles, Diván, Mario José Adv. Sci. Technol. Eng. Syst. J. 2(3), 12-23 (2017); View Description Low Probability of Interception Beampattern Using Frequency Diverse Array Antenna Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. J. 2(3), 24-29 (2017); View Description Zero Trust Cloud Networks using Transport Access Control and High Availability Optical Bypass Switching Casimer DeCusatis, Piradon Liengtiraphan, Anthony Sager Adv. Sci. Technol. Eng. Syst. J. 2(3), 30-35 (2017); View Description A Derived Metrics as a Measurement to Support Efficient Requirements Analysis and Release Management Indranil Nath Adv. Sci. Technol. Eng. Syst. J. 2(3), 36-40 (2017); View Description Feedback device of temperature sensation for a myoelectric prosthetic hand Yuki Ueda, Chiharu Ishii Adv. Sci. Technol. Eng. Syst. J. 2(3), 41-40 (2017); View Description Deep venous thrombus characterization: ultrasonography, elastography and scattering operator Thibaud Berthomier, Ali Mansour, Luc Bressollette, Frédéric Le Roy, Dominique Mottier Adv. Sci. Technol. Eng. Syst. J. 2(3), 48-59 (2017); View Description Improving customs’ border control by creating a reference database of cargo inspection X-ray images Selina Kolokytha, Alexander Flisch, Thomas Lüthi, Mathieu Plamondon, Adrian Schwaninger, Wicher Vasser, Diana Hardmeier, Marius Costin, Caroline Vienne, Frank Sukowski, Ulf Hassler, Irène Dorion, Najib Gadi, Serge Maitrejean, Abraham Marciano, Andrea Canonica, Eric Rochat, Ger Koomen, Micha Slegt Adv. Sci. Technol. Eng. Syst. J. 2(3), 60-66 (2017); View Description Aviation Navigation with Use of Polarimetric Technologies Arsen Klochan, Ali Al-Ammouri, Viktor Romanenko, Vladimir Tronko Adv. Sci. Technol. Eng. Syst. J. 2(3), 67-72 (2017); View Description Optimization of Multi-standard Transmitter Architecture Using Single-Double Conversion Technique Used for Rescue Operations Riadh Essaadali, Said Aliouane, Chokri Jebali and Ammar Kouki Adv. Sci. Technol. Eng. Syst. J. 2(3), 73-81 (2017); View Description Singular Integral Equations in Electromagnetic Waves Reflection Modeling A. S. Ilinskiy, T. N. Galishnikova Adv. Sci. Technol. Eng. Syst. J. 2(3), 82-87 (2017); View Description Methodology for Management of Information Security in Industrial Control Systems: A Proof of Concept aligned with Enterprise Objectives. Fabian Bustamante, Walter Fuertes, Paul Diaz, Theofilos Toulqueridis Adv. Sci. Technol. Eng. Syst. J. 2(3), 88-99 (2017); View Description Dependence-Based Segmentation Approach for Detecting Morpheme Boundaries Ahmed Khorsi, Abeer Alsheddi Adv. Sci. Technol. Eng. Syst. J. 2(3), 100-110 (2017); View Description Paper Improving Rule Based Stemmers to Solve Some Special Cases of Arabic Language Soufiane Farrah, Hanane El Manssouri, Ziyati Elhoussaine, Mohamed Ouzzif Adv. Sci. Technol. Eng. Syst. J. 2(3), 111-115 (2017); View Description Medical imbalanced data classification Sara Belarouci, Mohammed Amine Chikh Adv. Sci. Technol. Eng. Syst. J. 2(3), 116-124 (2017); View Description ADOxx Modelling Method Conceptualization Environment Nesat Efendioglu, Robert Woitsch, Wilfrid Utz, Damiano Falcioni Adv. Sci. Technol. Eng. Syst. J. 2(3), 125-136 (2017); View Description GPSR+Predict: An Enhancement for GPSR to Make Smart Routing Decision by Anticipating Movement of Vehicles in VANETs Zineb Squalli Houssaini, Imane Zaimi, Mohammed Oumsis, Saïd El Alaoui Ouatik Adv. Sci. Technol. Eng. Syst. J. 2(3), 137-146 (2017); View Description Optimal Synthesis of Universal Space Vector Digital Algorithm for Matrix Converters Adrian Popovici, Mircea Băbăiţă, Petru Papazian Adv. Sci. Technol. Eng. Syst. J. 2(3), 147-152 (2017); View Description Control design for axial flux permanent magnet synchronous motor which operates above the nominal speed Xuan Minh Tran, Nhu Hien Nguyen, Quoc Tuan Duong Adv. Sci. Technol. Eng. Syst. J. 2(3), 153-159 (2017); View Description A synchronizing second order sliding mode control applied to decentralized time delayed multi−agent robotic systems: Stability Proof Marwa Fathallah, Fatma Abdelhedi, Nabil Derbel Adv. Sci. Technol. Eng. Syst. J. 2(3), 160-170 (2017); View Description Fault Diagnosis and Tolerant Control Using Observer Banks Applied to Continuous Stirred Tank Reactor Martin F. Pico, Eduardo J. Adam Adv. Sci. Technol. Eng. Syst. J. 2(3), 171-181 (2017); View Description Development and Validation of a Heat Pump System Model Using Artificial Neural Network Nabil Nassif, Jordan Gooden Adv. Sci. Technol. Eng. Syst. J. 2(3), 182-185 (2017); View Description Assessment of the usefulness and appeal of stigma-stop by psychology students: a serious game designed to reduce the stigma of mental illness Adolfo J. Cangas, Noelia Navarro, Juan J. Ojeda, Diego Cangas, Jose A. Piedra, José Gallego Adv. Sci. Technol. Eng. Syst. 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J. 2(3), 241-244 (2017); View Description Energy-Efficient Mobile Sensing in Distributed Multi-Agent Sensor Networks Minh T. Nguyen Adv. Sci. Technol. Eng. Syst. J. 2(3), 245-253 (2017); View Description Validity and efficiency of conformal anomaly detection on big distributed data Ilia Nouretdinov Adv. Sci. Technol. Eng. Syst. J. 2(3), 254-267 (2017); View Description S-Parameters Optimization in both Segmented and Unsegmented Insulated TSV upto 40GHz Frequency Juma Mary Atieno, Xuliang Zhang, HE Song Bai Adv. Sci. Technol. Eng. Syst. J. 2(3), 268-276 (2017); View Description Synthesis of Important Design Criteria for Future Vehicle Electric System Lisa Braun, Eric Sax Adv. Sci. Technol. Eng. Syst. J. 2(3), 277-283 (2017); View Description Gestural Interaction for Virtual Reality Environments through Data Gloves G. Rodriguez, N. Jofre, Y. Alvarado, J. Fernández, R. Guerrero Adv. Sci. Technol. Eng. Syst. 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J. 2(3), 520-531 (2017); View Description Homemade array of surface coils implementation for small animal magnetic resonance imaging Fernando Yepes-Calderon, Olivier Beuf Adv. Sci. Technol. Eng. Syst. J. 2(3), 532-539 (2017); View Description An Encryption Key for Secure Authentication: The Dynamic Solution Zubayr Khalid, Pritam Paul, Khabbab Zakaria, Himadri Nath Saha Adv. Sci. Technol. Eng. Syst. J. 2(3), 540-544 (2017); View Description Multi-Domain Virtual Network Embedding with Coordinated Link Mapping Shuopeng Li, Mohand Yazid Saidi, Ken Chen Adv. Sci. Technol. Eng. Syst. J. 2(3), 545-552 (2017); View Description Semantic-less Breach Detection of Polymorphic Malware in Federated Cloud." Advances in Science, Technology and Engineering Systems Journal 2, no. 3 (June 2017): 553–61. http://dx.doi.org/10.25046/aj020371.

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"Electronic Credit Card Fraud Detection System by Collaboration of Machine Learning Models." International Journal of Innovative Technology and Exploring Engineering 8, no. 12S (December 26, 2019): 92–94. http://dx.doi.org/10.35940/ijitee.l1028.10812s19.

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In the financial industrial sector the lightning growth and participation of internet-based transactional events give rise to malicious activities like a fraud that result in financial loss. The malicious activities have no continuous pattern their pattern, behavior, working always keep on changing with the increasing growth in technology. Every time a new technology comes in the market the hoaxer study about that technology and implement malicious activity through the learned technology and internet-based activities. The hoaxer analyzes the behavior patterns of consumers to execute the plan of fraud to cause loss to the consumer. So to overcome this problem of fraud, hoax, cheat in the financial sector a fraud identification system is needed to identify the cheating, fraud and alike activities in internet-based money transactions by employing machine learning techniques. This presented paper focuses on fraud activities that cannot be detected manually by carrying out research and examine the results of logistic regression, decision tree and support vector machine. A dataset of electronic payment card is taken from European electronic cardholders, the machine learning techniques are applied on the unstructured and process-free data.

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Obeidat, Ibrahim, and Mazen AlZubi. "DEVELOPING A FASTER PATTERN MATCHING ALGORITHMS FOR INTRUSION DETECTION SYSTEM." International Journal of Computing, September 30, 2019, 278–84. http://dx.doi.org/10.47839/ijc.18.3.1520.

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Fast pattern matching algorithms mostly used by IDS, which are considered one of the important systems used to monitor and analyze host and network traffic. Their main function is to detect various types of malicious and malware files by examining incoming and outgoing data through the network. As the network speed growing, the malicious behavior and malware files are increasing; the pattern matching algorithms must be faster. In this research paper we are presenting a new method of pattern matching, which could be a platform for enhancement in the future. In this field, researchers spared no efforts to introduce fast algorithms for pattern matching. The Most popular algorithms are Boyer-Moore, Aho–Corasick, Naïve String search, Rabin Karp String Search and Knuth–Morris–Pratt. Based on studying these techniques we are developing algorithms that process the text data, using different algorithm technique and then we’ll test the performance and compare the processing time with the fastest proven pattern matching algorithms available. Document the result and draw the overall conclusion.

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"Privacy Protection Against Insider Attacks." International Journal of Engineering and Advanced Technology 9, no. 5 (June 30, 2020): 576–78. http://dx.doi.org/10.35940/ijeat.e9744.069520.

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A growing number of public and private sector organizations are recognizing insider threats as a critical area. In response, many steps are taken to defend assets against risks posed by employees and third-party trust. Insiders pose unique challenges for defenders. Traditional security tools are unlikely to audit insiders, let alone privileged users who have a potentially malicious intent. Although a high-risk activity, it is common to see users sharing passwords between colleagues or subordinates, defeating the purpose of authentication. This increases chances of Insider Attacks (IA), as it is hard to identify malicious insiders, given an attacker is entrusted with highly privileged access to read and write operations. Information Technology Organizations employ many workers with varying level of access, and every user is authenticated with unique login credenti¬als. Controls need to be put in place in order to secure the systems, since it can hamper login patterns. Research indicates that by analysis of system calls (SCs) that are generated upon user login can detect intrusions and read such patterns that are against the normal operations of the system. Information Technology Organizations employ many workers with varying level of access, and no two users have same login behavior. Given every user has a unique login pattern, this work proposes a system called Privacy Protection Against Insider Attacks (PPIA) which learns the login pattern of each user that is authenticated and employs data mining concepts to read user behavior and endeavors to detect insider attacks .Experimental results indicate that the approach is very effective and accurate..

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Novikova, Evgenia, Polina Belimova, Alena Dzhumagulova, Mikhail Bestuzhev, Yulia Bezbakh, Aleksandr Volosiuk, Andrey Balkanskii, and Alexei Lavrov. "Usability Assessment of the Visualization-Driven Approaches to the HVAC Data Exploration." Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2, December 17, 2020, paper17–1—paper17–12. http://dx.doi.org/10.51130/graphicon-2020-2-3-17.

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Application of the Internet-connected operational devices in the heating, ventilation and conditioning (HVAC) systems has extended the cyber-attack surface by introducing different malicious scenarios. The analysis of the HVAC data may provide insight on typical patterns of the system operations. Implementation of the thoroughly elaborated visualization models may significantly increase the efficiency of the suspicious activity identification in the HVAC systems. In the paper we present the results of the laboratory usability testing of three visualization models used to analyze HVAC data – matrix-based visualization technique, non-linear multidimensional visualization technique RadViz and timeline chart. Matrix-based visualization and RadViz visualization are often used in anomaly detection process, while timeline charts are a traditional way to present operational HVAC data. We describe the experiment design and discuss the results obtained. The usability testing revealed advantages and limitations of these visualization techniques in behavior pattern and anomaly identification tasks. The results can further serve as guidelines for task-dependent selection of a visualization technique.

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"Detection of Malware attacks in smart phones using Machine Learning." International Journal of Innovative Technology and Exploring Engineering 9, no. 1 (November 10, 2019): 4396–400. http://dx.doi.org/10.35940/ijitee.a5082.119119.

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In recent years, security has become progressively vital in mobile devices. The biggest security problems in android devices are malware attack which has been exposed to different threats. The volume of new applications by the production of mobile devices and their related app-stores is too big to manually examine the each and every application for malicious behavior. Installing applications which may leads to security vulnerabilities on the smart phones request access to sensitive information. There are various malwares can attack android device namely virus, worms, Botnet, Trojans, Backdoor and Root kits due to these attacks the users is compromised by privacy. Root kits and viruses in mobile phone and IoT devices improve along with smart device versions are very difficult to detect or to the least costly. There are 3 places where the trace of these root kits / virus is visible namely CPU, Baseband and Memory. In the new approach we will use machine learning to detect “anomaly” usage pattern and a remote (master server) will analyze and verify the presence of such threats. This research work aims to develop a pipeline to investigate if any application present in a smart device is a malware or not. This pipeline uses HMM algorithm to read anomaly in application behavior, deep learning with Deep Belief Networks (DBN) to classify application events, and bootstrapping algorithm using random forest to categorize the application itself after malware or benign

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Peng, Tu, Shuliang Wang, Jing Geng, Qinsi Wang, Yun Yang, and Kang Zhang. "Verification of the Instantiation and Integration of Security Patterns." Journal of Web Engineering, August 20, 2020. http://dx.doi.org/10.13052/jwe1540-9589.19347.

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As software applications suffer from increasing malicious attacks, security becomes a critically important issue for software development. To avoid security problems and increase efficiency, a large software system design may reuse good security solutions for existing security patterns. While security patterns document expert solutions to common security problems and capture well-examined practices on secure software design, implementing them in a particular context (pattern instantiation) and composing them with other related patterns (pattern integration) are prone to flaws and may break expected security properties. In this paper, we present an approach to verify security patterns instantiation and integration automatically. We offer formal definitions for security pattern instantiation and integration, and establish rules to transform sequence diagrams (representing the behaviors of security patterns) to expressions in Milner’s Calculus of Communicating Systems (CCS). We prove the correctness of the proposed transformation, and propose an algorithm to carry out this transformation automatically. In particular, we formally specify the alternative flows of UML sequence diagrams guarded by constraint conditions, which allows us to model choice making behaviors of security patterns precisely. The properties of the instantiation and integration can be verified by model checking against their CCS expressions. Flaws of instantiation and integration can, therefore, be discovered early in the design stage. We use two case studies to illustrate our approach and show the capability to prove security in integration and detect design errors in instantiation respectively.

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Journal articles on the topic 'Malicious behavior pattern'

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