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Journal articles on the topic 'Security of machine learning classifiers'

Author: Grafiati

Published: 14 March 2023

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1

Atnafu, Surafel Mehari, and Prof (Dr ). Anuja Kumar Acharya. "Comparative Analysis of Intrusion Detection Attack Based on Machine Learning Classifiers." Indian Journal of Artificial Intelligence and Neural Networking 1, no. 2 (2021): 22–28. http://dx.doi.org/10.35940/ijainn.b1025.041221.

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In current day information transmitted from one place to another by using network communication technology. Due to such transmission of information, networking system required a high security environment. The main strategy to secure this environment is to correctly identify the packet and detect if the packet contains a malicious and any illegal activity happened in network environments. To accomplish this, we use intrusion detection system (IDS). Intrusion detection is a security technology that design detects and automatically alert or notify to a responsible person. However, creating an eff

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Atnafu, Surafel Mehari, and Prof (Dr ). Anuja Kumar Acharya. "Comparative Analysis of Intrusion Detection Attack Based on Machine Learning Classifiers." Indian Journal of Artificial Intelligence and Neural Networking 1, no. 2 (2021): 22–28. http://dx.doi.org/10.54105/ijainn.b1025.041221.

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In current day information transmitted from one place to another by using network communication technology. Due to such transmission of information, networking system required a high security environment. The main strategy to secure this environment is to correctly identify the packet and detect if the packet contains a malicious and any illegal activity happened in network environments. To accomplish this, we use intrusion detection system (IDS). Intrusion detection is a security technology that design detects and automatically alert or notify to a responsible person. However, creating an eff

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ALGorain, Fahad T., and John A. Clark. "Covering Arrays ML HPO for Static Malware Detection." Eng 4, no. 1 (2023): 543–54. http://dx.doi.org/10.3390/eng4010032.

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Malware classification is a well-known problem in computer security. Hyper-parameter optimisation (HPO) using covering arrays (CAs) is a novel approach that can enhance machine learning classifier accuracy. The tuning of machine learning (ML) classifiers to increase classification accuracy is needed nowadays, especially with newly evolving malware. Four machine learning techniques were tuned using cAgen, a tool for generating covering arrays. The results show that cAgen is an efficient approach to achieve the optimal parameter choices for ML techniques. Moreover, the covering array shows a sig

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Katzir, Ziv, and Yuval Elovici. "Quantifying the resilience of machine learning classifiers used for cyber security." Expert Systems with Applications 92 (February 2018): 419–29. http://dx.doi.org/10.1016/j.eswa.2017.09.053.

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Gongada, Sandhya Rani, Muktevi Chakravarthy, and Bhukya Mangu. "Power system contingency classification using machine learning technique." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3091–98. http://dx.doi.org/10.11591/eei.v11i6.4031.

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One of the most effective ways for estimating the impact and severity of line failures on the static security of the power system is contingency analysis. The contingency categorization approach uses the overall performance index to measure the system's severity (OPI). The newton raphson (NR) load flow technique is used to extract network variables in a contingency situation for each transmission line failure. Static security is categorised into five categories in this paper: secure (S), critically secure (CS), insecure (IS), highly insecure (HIS), and most insecure (MIS). The K closest neighb

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Mehanović, Dželila, and Jasmin Kevrić. "Phishing Website Detection Using Machine Learning Classifiers Optimized by Feature Selection." Traitement du Signal 37, no. 4 (2020): 563–69. http://dx.doi.org/10.18280/ts.370403.

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Security is one of the most actual topics in the online world. Lists of security threats are constantly updated. One of those threats are phishing websites. In this work, we address the problem of phishing websites classification. Three classifiers were used: K-Nearest Neighbor, Decision Tree and Random Forest with the feature selection methods from Weka. Achieved accuracy was 100% and number of features was decreased to seven. Moreover, when we decreased the number of features, we decreased time to build models too. Time for Random Forest was decreased from the initial 2.88s and 3.05s for per

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Deshmukh, Miss Maithili, and Dr M. A. Pund. "Implementation Paper on Network Data Verification Using Machine Learning Classifiers Based on Reduced Feature Dimensions." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 2921–24. http://dx.doi.org/10.22214/ijraset.2022.41938.

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Abstract: With the rapid development of network-based applications, new risks arise and extra security mechanisms require additional attention to enhance speed and accuracy. Although many new security tools are developed, the rapid rise of malicious activity may be a major problem and therefore the ever-evolving attacks pose serious threats to network security. Network administrators rely heavily on intrusion detection systems to detect such network intrusion activity. a serious approach is machine learning methods for intrusion detection, where we learn models from data to differentiate betwe

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Runwal, Akshat. "Anomaly based Intrusion Detection System using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 255–60. http://dx.doi.org/10.22214/ijraset.2021.37955.

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Abstract: Attacks on the computer infrastructures are becoming an increasingly serious issue. The problem is ubiquitous and we need a reliable system to prevent it. An anomaly detection-based network intrusion detection system is vital to any security framework within a computer network. The existing Intrusion detection system have a high detection rate but they also have mendacious alert rates. With the use of Machine Learning, we can implement an efficient and reliable model for Intrusion detection and stop some of the hazardous attacks in the network. This paper focuses on detailed study on

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Abdulrezzak, Sarah, and Firas Sabir. "An Empirical Investigation on Snort NIDS versus Supervised Machine Learning Classifiers." Journal of Engineering 29, no. 2 (2023): 164–78. http://dx.doi.org/10.31026/j.eng.2023.02.11.

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With the vast usage of network services, Security became an important issue for all network types. Various techniques emerged to grant network security; among them is Network Intrusion Detection System (NIDS). Many extant NIDSs actively work against various intrusions, but there are still a number of performance issues including high false alarm rates, and numerous undetected attacks. To keep up with these attacks, some of the academic researchers turned towards machine learning (ML) techniques to create software that automatically predict intrusive and abnormal traffic, another approach is to

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Singh, Ravi, and Virender Ranga. "Performance Evaluation of Machine Learning Classifiers on Internet of Things Security Dataset." International Journal of Control and Automation 11, no. 5 (2018): 11–24. http://dx.doi.org/10.14257/ijca.2018.11.5.02.

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Deshmukh, Miss Maithili, and Dr M. A. Pund. "Review Paper on Network Data Verification Using Machine Learning Classifiers Based On Reduced Feature Dimensions." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 1592–95. http://dx.doi.org/10.22214/ijraset.2022.41586.

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Abstract: With the rapid development of network-based applications, new risks arise and additional security mechanisms require additional attention to improve speed and accuracy. Although many new security tools have been developed, the rapid rise of malicious activity is a serious problem and the ever-evolving attacks pose serious threats to network security. Network administrators rely heavily on intrusion detection systems to detect such network intrusion activity. A major approach is machine learning methods for intrusion detection, where we learn models from data to differentiate between

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Alkaaf, Howida Abuabker, Aida Ali, Siti Mariyam Shamsuddin, and Shafaatunnur Hassan. "Exploring permissions in android applications using ensemble-based extra tree feature selection." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 1 (2020): 543. http://dx.doi.org/10.11591/ijeecs.v19.i1.pp543-552.

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<span>The fast development of mobile apps and its usage has led to increase the risk of exploiting user privacy. One method used in Android security mechanism is permission control that restricts the access of apps to core facilities of devices. However, that permissions could be exploited by attackers when granting certain combinations of permissions. So, the aim of this paper is to explore the pattern of malware apps based on analyzing permissions by proposing framework utilizing feature selection based on ensemble extra tree classifier method and machine learning classifier. The used

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S.R., Chandrasekaran, and Dr Sabiyath Fatima N. "Speculating the Threat of Cardiovascular Disease Using Classifiers with User-Focused Security Evaluations." Webology 19, no. 1 (2022): 5529–46. http://dx.doi.org/10.14704/web/v19i1/web19372.

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In recent decades, cardiovascular disease (CVD) is the most common type of disease that is prevailing all over the world. It is a class of diseases that involve the heart and its vessels. Strokes and heart attacks are normally critical events that are largely provoked by congestion that restricts blood from streaming to the parts of the body. The principle aim of this research is to find the feature that accounts for cardiovascular disease risks. The collection of data from the hospitals and laboratories can determine the risk of patients having cardiovascular disease by analysing the trends a

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Sharma, Shweta. "OVERVIEW OF MACHINE LEARNING IN CYBERSECURITY COMPARATIVE ANALYSIS OF CLASSIFIERS USING WEKA." Journal of University of Shanghai for Science and Technology 23, no. 08 (2021): 334–43. http://dx.doi.org/10.51201/jusst/21/08385.

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Technologies have made a drastic change over years from mainframe computers to laptops, from telephone to cellular phone everything is changing and becoming digital. The online platform is the new way of working whether it is related to education, social gathering or business everything is going online which is easy, comfortable and consumes less time. Smart tv smartphones smartwatches that come under the category of IoT has been deployed all over the world nowadays, features like voice recognition system face detection system have become a crucial part of the most of the smart device. Nowaday

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K, Poojitha. "Detection of Malware in Android Phones Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 3344–47. http://dx.doi.org/10.22214/ijraset.2022.45726.

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Abstract: In a major cyber security scare, around 1.5 crore Android devices in India have been infected by malware without the knowledge of the users. According to a report by cyber security solution firm Check Point Research in 2020, a new variant of mobile malware has quietly infected around 2.5 crore devices worldwide. Malware is any type of malicious software or code designed to harm a user's device, such as trojans, adware, ransomware, spyware, viruses or phishing apps. The permissions and API-calls are extracted from all Android applications, and both were included as features in the dat

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Khonde, Shraddha R., and Venugopal Ulagamuthalvi. "Hybrid Architecture for Distributed Intrusion Detection System Using Semi-supervised Classifiers in Ensemble Approach." Advances in Modelling and Analysis B 63, no. 1-4 (2020): 10–19. http://dx.doi.org/10.18280/ama_b.631-403.

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Security of data is becoming a big treat today because of modern attacks. All the data passing through network is at risk as intruders can easily access and modify data. Security to the network is provided using Intrusion Detection System (IDS) which helps to monitor and analyze each packet entering or passing through the network. In this paper hybrid architecture for IDS is proposed which can work as an intelligent system in distributed environment. Proposed system makes use of semi-supervised machine learning classifiers into an ensemble approach. Classifiers used are Support vector machine,

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Shibaikin, Sergei, Vladimir Nikulin, and Andrei Abbakumov. "Analysis of machine learning methods for computer systems to ensure safety from fraudulent texts." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2020, no. 1 (2020): 29–40. http://dx.doi.org/10.24143/2072-9502-2020-1-29-40.

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IT Security is an essential condition for functioning of each company whose work is related to the information storage. Various models for detecting fraudulent texts including a support vector machine, neural networks, logistic regression, and a naive Bayes classifier, have been analyzed. It is proposed to increase the efficiency of detection of fraudulent messages by combining classifiers in ensembles. The metaclassifier allows to consider the accuracy values of all analyzers, involving in the work the construction of the weight matrix and the characteristic that determines the minimum accura

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Mahfouz, Ahmed, Abdullah Abuhussein, Deepak Venugopal, and Sajjan Shiva. "Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset." Future Internet 12, no. 11 (2020): 180. http://dx.doi.org/10.3390/fi12110180.

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Due to the extensive use of computer networks, new risks have arisen, and improving the speed and accuracy of security mechanisms has become a critical need. Although new security tools have been developed, the fast growth of malicious activities continues to be a pressing issue that creates severe threats to network security. Classical security tools such as firewalls are used as a first-line defense against security problems. However, firewalls do not entirely or perfectly eliminate intrusions. Thus, network administrators rely heavily on intrusion detection systems (IDSs) to detect such net

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Chinguwo, Michael Richard, and R. Dhanalakshmi. "Detecting Cloud Based Phishing Attacks Using Stacking Ensemble Machine Learning Technique." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 360–67. http://dx.doi.org/10.22214/ijraset.2023.49422.

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Abstract: Cloud computing enables users to access computing services over the Internet, but this also presents a security risk due to the anonymous nature of the Internet. Social engineering attacks are one of the most common security breaches in cloud computing, where attackers trick cloud users to reveal sensitive information. Detecting phishing attacks in cloud computing is challenging, and various solutions have been proposed, including rule-based and anomaly-based detection methods. Machine learning techniques have proven to be effective in detecting and classifying phishing attacks, part

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Alothman, Zainab, Mouhammd Alkasassbeh, and Sherenaz Al-Haj Baddar. "An efficient approach to detect IoT botnet attacks using machine learning." Journal of High Speed Networks 26, no. 3 (2020): 241–54. http://dx.doi.org/10.3233/jhs-200641.

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The numerous security loopholes in the design and implementation of many IoT devices have rendered them an easy target for botnet attacks. Several approaches to implement behavioral IoT botnet attacks detection have been explored, including machine learning. The main goal of previous studies was to achieve the highest possible accuracy in distinguishing normal from malicious IoT traffic, with minimal regard to the identification of the particular type of attack that is being launched. In this study, we present a machine learning based approach for detecting IoT botnet attacks that not only hel

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Pise, Nitin. "APPLICATION OF MACHINE LEARNING FOR INTRUSION DETECTION SYSTEM." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (2021): 314–23. http://dx.doi.org/10.17762/itii.v9i1.134.

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Due to Covid-19 pandemic, the most of the organizations have permitted their employees to work from home. Also, it is every essential to have security at the highest level so that information will flow in the safe and trusted environment between the different organizations. There is always threat of misuses and different intrusions for communication of the data securely over the internet. As more and more people are using online transactions for the different purposes, it is found that the cyber attackers have become more active. Three in four organizations have faced the different cyber-attac

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Abid, Adnan, Ansar Abbas, Adel Khelifi, Muhammad Shoaib Farooq, Razi Iqbal, and Uzma Farooq. "An architectural framework for information integration using machine learning approaches for smart city security profiling." International Journal of Distributed Sensor Networks 16, no. 10 (2020): 155014772096547. http://dx.doi.org/10.1177/1550147720965473.

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In the past few decades, the whole world has been badly affected by terrorism and other law-and-order situations. The newspapers have been covering terrorism and other law-and-order issues with relevant details. However, to the best of our knowledge, there is no existing information system that is capable of accumulating and analyzing these events to help in devising strategies to avoid and minimize such incidents in future. This research aims to provide a generic architectural framework to semi-automatically accumulate law-and-order-related news through different news portals and classify the

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Shroff, Jugal, Rahee Walambe, Sunil Kumar Singh, and Ketan Kotecha. "Enhanced Security Against Volumetric DDoS Attacks Using Adversarial Machine Learning." Wireless Communications and Mobile Computing 2022 (March 11, 2022): 1–10. http://dx.doi.org/10.1155/2022/5757164.

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With the increasing number of Internet users, cybersecurity is becoming more and more critical. Denial of service (DoS) and distributed denial of service (DDoS) attacks are two of the most common types of attacks that can severely affect a website or a server and make them unavailable to other users. The number of DDoS attacks increased by 55% between the period January 2020 and March 2021. Some approaches for detecting the DoS and DDoS attacks employing different machine learning and deep learning techniques are reported in the literature. Recently, it is also observed that the attackers have

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Khan, Rijwan, Akhilesh Kumar Srivastava, Mahima Gupta, Pallavi Kumari, and Santosh Kumar. "Medicolite-Machine Learning-Based Patient Care Model." Computational Intelligence and Neuroscience 2022 (January 25, 2022): 1–12. http://dx.doi.org/10.1155/2022/8109147.

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This paper discusses the machine learning effect on healthcare and the development of an application named “Medicolite” in which various modules have been developed for convenience with health-related problems like issues with diet. It also provides online doctor appointments from home and medication through the phone. A healthcare system is “Smart” when it can decide on its own and can prescribe patients life-saving drugs. Machine learning helps in capturing data that are large and contain sensitive information about the patients, so data security is one of the important aspects of this syste

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Lee, Ting Rong, Je Sen Teh, Norziana Jamil, Jasy Liew Suet Yan, and Jiageng Chen. "Lightweight Block Cipher Security Evaluation Based on Machine Learning Classifiers and Active S-Boxes." IEEE Access 9 (2021): 134052–64. http://dx.doi.org/10.1109/access.2021.3116468.

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Adithya Nallamuthu, Suresh. "A Hybrid Genetic-Neuro Algorithm for Cloud Intrusion Detection System." Journal of Computational Science and Intelligent Technologies 1, no. 2 (2020): 15–25. http://dx.doi.org/10.53409/mnaa.jcsit20201203.

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The security for cloud network systems is essential and significant to secure the data source from intruders and attacks. Implementing an intrusion detection system (IDS) for securing from those intruders and attacks is the best option. Many IDS models are presently based on different techniques and algorithms like machine learning and deep learning. In this research, IDS for the cloud computing environment is proposed. Here in this model, the genetic algorithm (GA) and back propagation neural network (BPNN) is used for attack detection and classification. The Canadian Institute for Cyber-secu

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Aljably, Randa, Yuan Tian, and Mznah Al-Rodhaan. "Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection." Security and Communication Networks 2020 (July 20, 2020): 1–14. http://dx.doi.org/10.1155/2020/5874935.

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Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. I

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Al-Zewairi, Malek, Sufyan Almajali, and Moussa Ayyash. "Unknown Security Attack Detection Using Shallow and Deep ANN Classifiers." Electronics 9, no. 12 (2020): 2006. http://dx.doi.org/10.3390/electronics9122006.

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Advancements in machine learning and artificial intelligence have been widely utilised in the security domain, including but not limited to intrusion detection techniques. With the large training datasets of modern traffic, intelligent algorithms and powerful machine learning tools, security researchers have been able to greatly improve on the intrusion detection models and enhance their ability to detect malicious traffic more accurately. Nonetheless, the problem of detecting completely unknown security attacks is still an open area of research. The enormous number of newly developed attacks

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Al-Akhras, Mousa, Mohammed Alawairdhi, Ali Alkoudari, and Samer Atawneh. "Using Machine Learning to Build a Classification Model for IoT Networks to Detect Attack Signatures." International journal of Computer Networks & Communications 12, no. 6 (2020): 99–116. http://dx.doi.org/10.5121/ijcnc.2020.12607.

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Internet of things (IoT) has led to several security threats and challenges within society. Regardless of the benefits that it has brought with it to the society, IoT could compromise the security and privacy of individuals and companies at various levels. Denial of Service (DoS) and Distributed DoS (DDoS) attacks, among others, are the most common attack types that face the IoT networks. To counter such attacks, companies should implement an efficient classification/detection model, which is not an easy task. This paper proposes a classification model to examine the effectiveness of several m

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Shatnawi, Ahmed S., Aya Jaradat, Tuqa Bani Yaseen, Eyad Taqieddin, Mahmoud Al-Ayyoub, and Dheya Mustafa. "An Android Malware Detection Leveraging Machine Learning." Wireless Communications and Mobile Computing 2022 (May 6, 2022): 1–12. http://dx.doi.org/10.1155/2022/1830201.

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Android applications have recently witnessed a pronounced progress, making them among the fastest growing technological fields to thrive and advance. However, such level of growth does not evolve without some cost. This particularly involves increased security threats that the underlying applications and their users usually fall prey to. As malware becomes increasingly more capable of penetrating these applications and exploiting them in suspicious actions, the need for active research endeavors to counter these malicious programs becomes imminent. Some of the studies are based on dynamic anal

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Jaradat, Ameera S., Malek M. Barhoush, and Rawan S. Bani Easa. "Network intrusion detection system: machine learning approach." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 1151. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp1151-1158.

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The main goal of intrusion detection system (IDS) is to monitor the network performance and to investigate any signs of any abnormalities over the network. Recently, intrusion detection systems employ machine learning techniques, due to the fact that machine learning techniques proved to have the ability of learning and adapting in addition to allowing a prompt response. This work proposes a model for intrusion detection and classification using machine learning techniques. The model first acquires the data set and transforms it in the proper format, then performs feature selection to pick out

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Khan, Riaz Ullah, Xiaosong Zhang, Rajesh Kumar, Abubakar Sharif, Noorbakhsh Amiri Golilarz, and Mamoun Alazab. "An Adaptive Multi-Layer Botnet Detection Technique Using Machine Learning Classifiers." Applied Sciences 9, no. 11 (2019): 2375. http://dx.doi.org/10.3390/app9112375.

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In recent years, the botnets have been the most common threats to network security since it exploits multiple malicious codes like a worm, Trojans, Rootkit, etc. The botnets have been used to carry phishing links, to perform attacks and provide malicious services on the internet. It is challenging to identify Peer-to-peer (P2P) botnets as compared to Internet Relay Chat (IRC), Hypertext Transfer Protocol (HTTP) and other types of botnets because P2P traffic has typical features of the centralization and distribution. To resolve the issues of P2P botnet identification, we propose an effective m

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Abed, Abdullah Suhail, Brwa Khalil Abdullah Ahmed, Sura Khalil Ibrahim, Musaddak Maher Abdul Zahra, Mohanad Ahmed Salih, and Refed Adnan Jaleel. "Development of an Integrate E-Medical System Using Software Defined Networking and Machine Learning." Webology 19, no. 1 (2022): 3410–18. http://dx.doi.org/10.14704/web/v19i1/web19224.

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Scholars and medical professionals have recognizes the importance of electronic medical monitoring services for tracking elderly people's health. These platforms generate a large amount of data, requiring privacy and data security. on the contrary, Using Software Defined Networking (SDN) to maintain network efficiency and flexibility, which is especially important in the case of healthcare observation, could be a viable solution. Moreover, machine learning can additionally utilized as a game changing tool which incorporated with SDN for optimal level of privacy and security. Even so, integrati

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Alsulaiman, Lama, and Saad Al-Ahmadi. "Performance Evaluation of Machine Learning Techniques for DOS Detection in Wireless Sensor Network." International Journal of Network Security & Its Applications 13, no. 2 (2021): 21–29. http://dx.doi.org/10.5121/ijnsa.2021.13202.

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The nature of Wireless Sensor Networks (WSN) and the widespread of using WSN introduce many security threats and attacks. An effective Intrusion Detection System (IDS) should be used to detect attacks. Detecting such an attack is challenging, especially the detection of Denial of Service (DoS) attacks. Machine learning classification techniques have been used as an approach for DoS detection. This paper conducted an experiment using Waikato Environment for Knowledge Analysis (WEKA)to evaluate the efficiency of five machine learning algorithms for detecting flooding, grayhole, blackhole, and sc

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Kanaker, Hasan, Nader Abdel Karim, Samer A.B. Awwad, Nurul H.A. Ismail, Jamal Zraqou, and Abdulla M. F. Al ali. "Trojan Horse Infection Detection in Cloud Based Environment Using Machine Learning." International Journal of Interactive Mobile Technologies (iJIM) 16, no. 24 (2022): 81–106. http://dx.doi.org/10.3991/ijim.v16i24.35763.

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Cloud computing technology is known as a distributed computing network, which consists of a large number of servers connected via the internet. This technology involves many worthwhile resources, such as applications, services, and large database storage. Users have the ability to access cloud services and resources through web services. Cloud computing provides a considerable number of benefits, such as effective virtualized resources, cost efficiency, self-service access, flexibility, and scalability. However, many security issues are present in cloud computing environment. One of the most c

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Gbenga*, Fadare Oluwaseun, Prof Adetunmbi Adebayo Olusola, Dr (Mrs) Oyinloye Oghenerukevwe Eloho, and Dr Mogaji Stephen Alaba. "Towards Optimization of Malware Detection using Chi-square Feature Selection on Ensemble Classifiers." International Journal of Engineering and Advanced Technology 10, no. 4 (2021): 254–62. http://dx.doi.org/10.35940/ijeat.d2359.0410421.

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The multiplication of malware variations is probably the greatest problem in PC security and the protection of information in form of source code against unauthorized access is a central issue in computer security. In recent times, machine learning has been extensively researched for malware detection and ensemble technique has been established to be highly effective in terms of detection accuracy. This paper proposes a framework that combines combining the exploit of both Chi-square as the feature selection method and eight ensemble learning classifiers on five base learners- K-Nearest Neighb

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Hammad, Baraa Tareq, Norziana Jamil, Ismail Taha Ahmed, Zuhaira Muhammad Zain, and Shakila Basheer. "Robust Malware Family Classification Using Effective Features and Classifiers." Applied Sciences 12, no. 15 (2022): 7877. http://dx.doi.org/10.3390/app12157877.

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Malware development has significantly increased recently, posing a serious security risk to both consumers and businesses. Malware developers continually find new ways to circumvent security research’s ongoing efforts to guard against malware attacks. Malware Classification (MC) entails labeling a class of malware to a specific sample, while malware detection merely entails finding malware without identifying which kind of malware it is. There are two main reasons why the most popular MC techniques have a low classification rate. First, Finding and developing accurate features requires highly

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Nigus, Mersha, and H. L. Shashirekha. "A Comparison of Machine Learning and Deep Learning Models for Predicting Household Food Security Status." International Journal of Electrical and Electronics Research 10, no. 2 (2022): 308–11. http://dx.doi.org/10.37391/ijeer.100241.

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ML and DL algorithms are becoming more popular to predict household food security status, which can be used by the governments and policymakers of the country to provide a food supply for the needy in case of emergency. ML models, namely: k-Nearest Neighbor (kNN), Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Multi-Layer Perceptron (MLP) and DL models, namely: Artificial Neural Network (ANN) and Convolutional Neural network (CNN) are investigated to predict household food security status in Household Income, Consumption and Expenditure (HICE) survey data of Ethiopia

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Bangira, Tsitsi, Silvia Maria Alfieri, Massimo Menenti, and Adriaan van Niekerk. "Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water." Remote Sensing 11, no. 11 (2019): 1351. http://dx.doi.org/10.3390/rs11111351.

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Small reservoirs play an important role in mining, industries, and agriculture, but storage levels or stage changes are very dynamic. Accurate and up-to-date maps of surface water storage and distribution are invaluable for informing decisions relating to water security, flood monitoring, and water resources management. Satellite remote sensing is an effective way of monitoring the dynamics of surface waterbodies over large areas. The European Space Agency (ESA) has recently launched constellations of Sentinel-1 (S1) and Sentinel-2 (S2) satellites carrying C-band synthetic aperture radar (SAR)

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Almaiah, Mohammed Amin, Omar Almomani, Adeeb Alsaaidah, et al. "Performance Investigation of Principal Component Analysis for Intrusion Detection System Using Different Support Vector Machine Kernels." Electronics 11, no. 21 (2022): 3571. http://dx.doi.org/10.3390/electronics11213571.

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The growing number of security threats has prompted the use of a variety of security techniques. The most common security tools for identifying and tracking intruders across diverse network domains are intrusion detection systems. Machine Learning classifiers have begun to be used in the detection of threats, thus increasing the intrusion detection systems’ performance. In this paper, the investigation model for an intrusion detection systems model based on the Principal Component Analysis feature selection technique and a different Support Vector Machine kernels classifier is present. The imp

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Thabtah, Fadi, and Firuz Kamalov. "Phishing Detection: A Case Analysis on Classifiers with Rules Using Machine Learning." Journal of Information & Knowledge Management 16, no. 04 (2017): 1750034. http://dx.doi.org/10.1142/s0219649217500344.

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A typical predictive approach in data mining that produces If-Then knowledge for decision making is rule-based classification. Rule-based classification includes a large number of algorithms that fall under the categories of covering, greedy, rule induction, and associative classification. These approaches have shown promising results due to the simplicity of the models generated and the user’s ability to understand, and maintain them. Phishing is one of the emergent online threats in web security domains that necessitates anti-phishing models with rules so users can easily differentiate among

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Azeez, Nureni Ayofe, Oluwanifise Ebunoluwa Odufuwa, Sanjay Misra, Jonathan Oluranti, and Robertas Damaševičius. "Windows PE Malware Detection Using Ensemble Learning." Informatics 8, no. 1 (2021): 10. http://dx.doi.org/10.3390/informatics8010010.

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In this Internet age, there are increasingly many threats to the security and safety of users daily. One of such threats is malicious software otherwise known as malware (ransomware, Trojans, viruses, etc.). The effect of this threat can lead to loss or malicious replacement of important information (such as bank account details, etc.). Malware creators have been able to bypass traditional methods of malware detection, which can be time-consuming and unreliable for unknown malware. This motivates the need for intelligent ways to detect malware, especially new malware which have not been evalua

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Guo, You, Hector Marco-Gisbert, and Paul Keir. "Mitigating Webshell Attacks through Machine Learning Techniques." Future Internet 12, no. 1 (2020): 12. http://dx.doi.org/10.3390/fi12010012.

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A webshell is a command execution environment in the form of web pages. It is often used by attackers as a backdoor tool for web server operations. Accurately detecting webshells is of great significance to web server protection. Most security products detect webshells based on feature-matching methods—matching input scripts against pre-built malicious code collections. The feature-matching method has a low detection rate for obfuscated webshells. However, with the help of machine learning algorithms, webshells can be detected more efficiently and accurately. In this paper, we propose a new PH

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Gumaste, Shweta, Narayan D. G., Sumedha Shinde, and Amit K. "Detection of DDoS Attacks in OpenStack-based Private Cloud Using Apache Spark." Journal of Telecommunications and Information Technology 4 (December 30, 2020): 62–71. http://dx.doi.org/10.26636/jtit.2020.146120.

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Security is a critical concern for cloud service providers. Distributed denial of service (DDoS) attacks are the most frequent of all cloud security threats, and the consequences of damage caused by DDoS are very serious. Thus, the design of an efficient DDoS detection system plays an important role in monitoring suspicious activity in the cloud. Real-time detection mechanisms operating in cloud environments and relying on machine learning algorithms and distributed processing are an important research issue. In this work, we propose a real-time detection of DDoS attacks using machine learning

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Bagui, Sikha, Dustin Mink, Subhash Bagui, et al. "Detecting Reconnaissance and Discovery Tactics from the MITRE ATT&CK Framework in Zeek Conn Logs Using Spark’s Machine Learning in the Big Data Framework." Sensors 22, no. 20 (2022): 7999. http://dx.doi.org/10.3390/s22207999.

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While computer networks and the massive amount of communication taking place on these networks grow, the amount of damage that can be done by network intrusions grows in tandem. The need is for an effective and scalable intrusion detection system (IDS) to address these potential damages that come with the growth of these networks. A great deal of contemporary research on near real-time IDS focuses on applying machine learning classifiers to labeled network intrusion datasets, but these datasets need be relevant pertaining to the currency of the network intrusions. This paper focuses on a newly

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Essa, Hasanain Ali Al, and Wesam S. Bhaya. "Network Attacks Detection Depend on Majority Voting – Weighted Average for Feature Selection and Various Machine Learning Approaches." Webology 19, no. 1 (2022): 2054–66. http://dx.doi.org/10.14704/web/v19i1/web19139.

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Due to the enormous growth in Internet usage and computer networks in recent years, new risks and challenges have arisen to network security. Among lots of security problems, network attack is a significant one. For instance, Distributed Denial of Service (DDoS) attacks have become appealing to intruders, and these have presented destructive threats to network infrastructures. Thus, Intrusion Detection Systems (IDSs) and Machine Learning (ML) approaches play a key role to detect such attacks effectively and efficiently. An essential part of several classification issues is the feature selectio

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Yang, Hao, Qin He, Zhenyan Liu, and Qian Zhang. "Malicious Encryption Traffic Detection Based on NLP." Security and Communication Networks 2021 (August 3, 2021): 1–10. http://dx.doi.org/10.1155/2021/9960822.

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The development of Internet and network applications has brought the development of encrypted communication technology. But on this basis, malicious traffic also uses encryption to avoid traditional security protection and detection. Traditional security protection and detection methods cannot accurately detect encrypted malicious traffic. In recent years, the rise of artificial intelligence allows us to use machine learning and deep learning methods to detect encrypted malicious traffic without decryption, and the detection results are very accurate. At present, the research on malicious encr

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Cho, Jaeik, Seonghyeon Gong, and Ken Choi. "A Study on High-Speed Outlier Detection Method of Network Abnormal Behavior Data Using Heterogeneous Multiple Classifiers." Applied Sciences 12, no. 3 (2022): 1011. http://dx.doi.org/10.3390/app12031011.

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As the complexity and scale of the network environment increase continuously, various methods to detect attacks and intrusions from network traffic by classifying normal and abnormal network behaviors show their limitations. The number of network traffic signatures is increasing exponentially to the extent that semi-realtime detection is not possible. However, machine learning-based intrusion detection only gives simple guidelines as simple contents of security events. This is why security data for a specific environment cannot be configured due to data noise, diversification, and continuous a

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Aslam, Muhammad, Dengpan Ye, Aqil Tariq, et al. "Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for SDN-Enabled IoT." Sensors 22, no. 7 (2022): 2697. http://dx.doi.org/10.3390/s22072697.

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The development of smart network infrastructure of the Internet of Things (IoT) faces the immense threat of sophisticated Distributed Denial-of-Services (DDoS) security attacks. The existing network security solutions of enterprise networks are significantly expensive and unscalable for IoT. The integration of recently developed Software Defined Networking (SDN) reduces a significant amount of computational overhead for IoT network devices and enables additional security measurements. At the prelude stage of SDN-enabled IoT network infrastructure, the sampling based security approach currently

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Hicham, Benradi, Chater Ahmed, and Lasfar Abdelali. "Face recognition method combining SVM machine learning and scale invariant feature transform." E3S Web of Conferences 351 (2022): 01033. http://dx.doi.org/10.1051/e3sconf/202235101033.

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Facial recognition is a method to identify an individual from his image. It has attracted the intention of a large number of researchers in the field of computer vision in recent years due to its wide scope of application in several areas (health, security, robotics, biometrics...). The operation of this technology, so much in demand in today's market, is based on the extraction of features from an input image using techniques such as SIFT, SURF, LBP... and comparing them with others from another image to confirm or assert the identity of an individual. In this paper, we have performed a compa

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