Journal articles on the topic 'Adaptive IDS'
To see the other types of publications on this topic, follow the link: Adaptive IDS.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Adaptive IDS.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Ninu, S. B., and S. Behin Sam. "Hybrid Enhanced Adaptive ACK IDS Scheme for MANETs." i-manager's Journal on Mobile Applications and Technologies 2, no. 2 (July 15, 2015): 19–26. http://dx.doi.org/10.26634/jmt.2.2.4872.
Full text
2
Lu, Yi, Menghan Liu, Jie Zhou, and Zhigang Li. "Intrusion Detection Method Based on Adaptive Clonal Genetic Algorithm and Backpropagation Neural Network." Security and Communication Networks 2021 (July 13, 2021): 1–12. http://dx.doi.org/10.1155/2021/9938586.
Full textAbstract:
Intrusion Detection System (IDS) is an important part of ensuring network security. When the system faces network attacks, it can identify the source of threats in a timely and accurate manner and adjust strategies to prevent hackers from intruding. Efficient IDS can identify external threats well, but traditional IDS has poor performance and low recognition accuracy. To improve the detection rate and accuracy of IDS, this paper proposes a novel ACGA-BPNN method based on adaptive clonal genetic algorithm (ACGA) and backpropagation neural network (BPNN). ACGA-BPNN is simulated on the KDD-CUP’99 and UNSW-NB15 data sets. The simulation results indicate that, in contrast to the methods based on simulated annealing (SA) and genetic algorithm (GA), the detection rate and accuracy of ACGA-BPNN are much higher than of GA-BPNN and SA-BPNN. In the classification results of KDD-CUP’99, the classification accuracy of ACGA-BPNN is 11% higher than GA-BPNN and 24.2% higher than SA-BPNN, and F-score reaches 99.0%. In addition, ACGA-BPNN has good global searchability and its convergence speed is higher than that of GA-BPNN and SA-BPNN. Furthermore, ACGA-BPNN significantly improves the overall detection performance of IDS.
3
Xue, Yu, Weiwei Jia, Xuejian Zhao, and Wei Pang. "An Evolutionary Computation Based Feature Selection Method for Intrusion Detection." Security and Communication Networks 2018 (October 9, 2018): 1–10. http://dx.doi.org/10.1155/2018/2492956.
Full textAbstract:
As the important elements of the Internet of Things system, wireless sensor network (WSN) has gradually become popular in many application fields. However, due to the openness of WSN, attackers can easily eavesdrop, intercept, and rebroadcast data packets. WSN has also faced many other security issues. Intrusion detection system (IDS) plays a pivotal part in data security protection of WSN. It can identify malicious activities that attempt to violate network security goals. Therefore, the development of effective intrusion detection technologies is very important. However, many dimensions of the datasets of IDS are irrelevant or redundant. This causes low detection speed and poor performance. Feature selection is thus introduced to reduce dimensions in IDS. At the same time, many evolutionary computing (EC) techniques were employed in feature selection. However, these techniques usually have just one Candidate Solution Generation Strategy (CSGS) and often fall into local optima when dealing with feature selection problems. The self-adaptive differential evolution (SaDE) algorithm is adopted in our paper to deal with feature selection problems for IDS. The adaptive mechanism and four effective CSGSs are used in SaDE. Through this method, an appropriate CSGS can be selected adaptively to generate new individuals during evolutionary process. Besides, we have also improved the control parameters of the SaDE. The K-Nearest Neighbour (KNN) is used for performance assessment for feature selection. KDDCUP99 dataset is employed in the experiments, and experimental results demonstrate that SaDE is more promising than the algorithms it compares.
4
Hsu, Po-Jen, Hung-Ling Yeh, Chia-Liang Tsai, Chia-Hua Chu, Fu-Chen Chen, and Chien-Yu Pan. "Effects of a Floor Hockey Intervention on Motor Proficiency, Physical Fitness, and Adaptive Development in Youths with Mild Intellectual Disabilities." International Journal of Environmental Research and Public Health 18, no. 13 (July 1, 2021): 7059. http://dx.doi.org/10.3390/ijerph18137059.
Full textAbstract:
This study examined the effects of a 12-week floor hockey training program on the motor proficiency, physical fitness, and adaptive development of youths with mild intellectual disabilities (IDs). A total of 54 youths with IDs were divided into two groups: a floor hockey exercise group (EG; n = 27; age, 16.59 ± 0.56 years) and a control group (CG; n = 27; age, 16.65 ± 0.63 years). The participants in the EG attended sessions of a floor hockey training program 3 times per week over a 12-week period. The CG group maintained their standard activities of daily living. The participants’ scores on the Bruininks–Oseretsky Test of Motor Proficiency, Second Edition, Brockport Physical Fitness Test, and traditional Chinese version of the teacher form of the Adaptive Behavior Assessment System, Second Edition, were obtained before and after the intervention. The results of the study indicate that the 12-week floor hockey training program significantly increased the participants’ scores for most indicators of motor proficiency (p < 0.01), physical fitness (p < 0.01), and adaptive development (p < 0.01). The findings provide evidence that physical activity interventions focusing on floor hockey training are a viable therapeutic option for treating youths with IDs.
5
Ramdane, Chikh, and Salim Chikhi. "A New Negative Selection Algorithm for Adaptive Network Intrusion Detection System." International Journal of Information Security and Privacy 8, no. 4 (October 2014): 1–25. http://dx.doi.org/10.4018/ijisp.2014100101.
Full textAbstract:
Negative Selection Algorithm (NSA) is one of the widely used techniques for Intrusion Detection Systems (IDS) designing. In this paper, the proposed is an IDS based on a new model of NSA namely HNSA-IDSA (Hybrid NSA for Intrusion Detection System Adaptation). The proposed system can detect unknown attacks; moreover can be adapted automatically when new profiles' changes of the system are detected. To determine the efficiency of the proposed approach, the standard KDD99 dataset was used for performing experiments. The obtained results show that the authors' mechanism outperforms some literature techniques providing variant important properties as high detection rate, low false positive, adaptability and new attacks detection.
6
Klidbary, Sajad Haghzad, Saeed Bagheri Shouraki, and Iman Esmaili Paeen Afrakoti. "An adaptive efficient memristive ink drop spread (IDS) computing system." Neural Computing and Applications 31, no. 11 (July 4, 2018): 7733–54. http://dx.doi.org/10.1007/s00521-018-3604-0.
Full text
7
Puchalska-Wasyl, Małgorzata M., and Tomasz Jankowski. "Do Internal Dialogues in Young Adults Depend on Mother-Father Incongruence in Parental Attitudes Assessed Retrospectively?" Journal of Family Issues 41, no. 5 (October 14, 2019): 667–91. http://dx.doi.org/10.1177/0192513x19881773.
Full textAbstract:
Mother-father incongruence in parental attitudes can cause conflict in the child. This may result in an experience of uncertainty that stimulates a person to engage in internal dialogues (IDs). Thus, we hypothesized that the greater the incongruence between the mother’s and the father’s parental attitudes, as assessed retrospectively by the child, the greater is the intensity of IDs in an adult offspring’s life. Participants were 92 women and 84 men aged between 20 years and 32 years. We used two methods: the Questionnaire of Retrospective Assessment of Parental Attitudes and the Internal Dialogical Activity Scale. We conducted a response survey analysis. Our hypothesis has been fully supported with regard to non-adaptive confronting IDs and general internal dialogical activity: the less the mother protects, and the more the father is overprotective, the greater is the intensity of these IDs. Our findings are discussed in light of the broader literature on parental attitudes and IDs.
8
Krishnan Sadhasivan, Dhanalakshmi, and Kannapiran Balasubramanian. "A Fusion of Multiagent Functionalities for Effective Intrusion Detection System." Security and Communication Networks 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/6216078.
Full textAbstract:
Provision of high security is one of the active research areas in the network applications. The failure in the centralized system based on the attacks provides less protection. Besides, the lack of update of new attacks arrival leads to the minimum accuracy of detection. The major focus of this paper is to improve the detection performance through the adaptive update of attacking information to the database. We propose an Adaptive Rule-Based Multiagent Intrusion Detection System (ARMA-IDS) to detect the anomalies in the real-time datasets such as KDD and SCADA. Besides, the feedback loop provides the necessary update of attacks in the database that leads to the improvement in the detection accuracy. The combination of the rules and responsibilities for multiagents effectively detects the anomaly behavior, misuse of response, or relay reports of gas/water pipeline data in KDD and SCADA, respectively. The comparative analysis of the proposed ARMA-IDS with the various existing path mining methods, namely, random forest, JRip, a combination of AdaBoost/JRip, and common path mining on the SCADA dataset conveys that the effectiveness of the proposed ARMA-IDS in the real-time fault monitoring. Moreover, the proposed ARMA-IDS offers the higher detection rate in the SCADA and KDD cup 1999 datasets.
9
Ibrahim, Nurudeen Mahmud, and Anazida Zainal. "An Adaptive Intrusion Detection Scheme for Cloud Computing." International Journal of Swarm Intelligence Research 10, no. 4 (October 2019): 53–70. http://dx.doi.org/10.4018/ijsir.2019100104.
Full textAbstract:
To provide dynamic resource management, live virtual machine migration is used to move a virtual machine from one host to another. However, virtual machine migration poses challenges to cloud intrusion detection systems because movement of VMs from one host to another makes it difficult to create a consistent normal profile for anomaly detection. Hence, there is a need to provide an adaptive anomaly detection system capable of adapting to changes that occur in the cloud data during VM migration. To achieve this, the authors proposed a scheme for adaptive IDS for Cloud computing. The proposed adaptive scheme is comprised of four components: an ant colony optimization-based feature selection component, a statistical time series change point detection component, adaptive classification, and model update component, and a detection component. The proposed adaptive scheme was evaluated using simulated datasets collected from vSphere and performance comparison shows improved performance over existing techniques.
10
Gao, Jianlei, Senchun Chai, Baihai Zhang, and Yuanqing Xia. "Research on Network Intrusion Detection Based on Incremental Extreme Learning Machine and Adaptive Principal Component Analysis." Energies 12, no. 7 (March 29, 2019): 1223. http://dx.doi.org/10.3390/en12071223.
Full textAbstract:
Recently, network attacks launched by malicious attackers have seriously affected modern life and enterprise production, and these network attack samples have the characteristic of type imbalance, which undoubtedly increases the difficulty of intrusion detection. In response to this problem, it would naturally be very meaningful to design an intrusion detection system (IDS) to effectively and quickly identify and detect malicious behaviors. In our work, we have proposed a method for an IDS-combined incremental extreme learning machine (I-ELM) with an adaptive principal component (A-PCA). In this method, the relevant features of network traffic are adaptively selected, where the best detection accuracy can then be obtained by I-ELM. We have used the NSL-KDD standard dataset and UNSW-NB15 standard dataset to evaluate the performance of our proposed method. Through analysis of the experimental results, we can see that our proposed method has better computation capacity, stronger generalization ability, and higher accuracy.
11
Suresh, Chandra K. H., Sule Ozev, and Ozgur Sinanoglu. "Adaptive Generation of Unique IDs for Digital Chips through Analog Excitation." ACM Transactions on Design Automation of Electronic Systems 20, no. 3 (June 24, 2015): 1–18. http://dx.doi.org/10.1145/2732408.
Full text
12
Garg, Monika, Karanvir Kaur, and Simerpreet Kaur. "Using 3GPP- A Secure IDS for MANETs." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (October 30, 2005): 518–21. http://dx.doi.org/10.24297/ijct.v4i2b2.3312.
Full textAbstract:
Mobile Ad hoc NETwork (MANET) is one of the important and unique applications. MANET does not require a fixednetwork infrastructure, every single node works as both a transmitter and a receiver. Nodes communicate directly witheach other when they are both within the same communication range else they can propagate message to neighbor nodesto pass the message. A new intrusion detection system named Enhanced Adaptive Acknowledgement (EAACK) speciallydesigned for MANETs. By the adoption of MRA scheme, EAACK is capable of detecting malicious nodes despite theexistence of false misbehavior report. The results will demonstrate positive performances against Watchdog, TWOACKand AACK in the cases of receiver collision, limited transmission power and false misbehavior report. EAACKdemonstrates higher malicious behavior detection rates in certain circumstances while does not greatly affect the networkperformances. EAACK is designed based on the Digital signature Algorithm (DSA) and RSA. Those techniques havedrawback due to network overhead. We need techniques for security like encryptions, hybrid encryption or sign encryptionto protect our message.
13
Yun, Dong-Yeol, Seung-Kwon Seo, Umer Zahid, and Chul-Jin Lee. "Deep Neural Network for Automatic Image Recognition of Engineering Diagrams." Applied Sciences 10, no. 11 (June 9, 2020): 4005. http://dx.doi.org/10.3390/app10114005.
Full textAbstract:
Piping and instrument diagrams (P&IDs) are a key component of the process industry; they contain information about the plant, including the instruments, lines, valves, and control logic. However, the complexity of these diagrams makes it difficult to extract the information automatically. In this study, we implement an object-detection method to recognize graphical symbols in P&IDs. The framework consists of three parts—region proposal, data annotation, and classification. Sequential image processing is applied as the region proposal step for P&IDs. After getting the proposed regions, the unsupervised learning methods, k-means, and deep adaptive clustering are implemented to decompose the detected dummy symbols and assign negative classes for them. By training a convolutional network, it becomes possible to classify the proposed regions and extract the symbolic information. The results indicate that the proposed framework delivers a superior symbol-recognition performance through dummy detection.
14
Ye, Ru Yi, Zhou Jiang, and Qi Wang. "Economics of Information Security Investment Integrated with IDS and Attacker’s Behavior." Applied Mechanics and Materials 631-632 (September 2014): 928–31. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.928.
Full textAbstract:
ROSI (Return On Security Investment) has attracted a great deal of attention in recent years. By inheriting Gordon and Loeb 2002 security breach probability function, we present an adaptive economics model of investment in information security integrating dynamic characteristics of outside threat probability and detective mechanism, and deduce some guidelines for optimal investment amount.
15
JOSHI, RAJANI R., and BHUVANESWARAN NATARAJAN. "PREDICTION AND ANALYSIS OF VACCINATION POSSIBILITIES IN AUTOIMMUNE DISEASES: AN APPLICATION OF ARTIFICIAL IMMUNE SYSTEM." Journal of Biological Systems 10, no. 02 (June 2002): 107–26. http://dx.doi.org/10.1142/s0218339002000536.
Full textAbstract:
We present an adaptive machine learning model of the humoral immune response. Antigens (epitopes/ids) and antibodies (paratopes/anti-ids) are represented here as sequences of single letter amino acid codes. The model effectively simulates dynamic affinity maturation, memory and associativity. Specific age-function is derived here based on recent experimental findings and is used to incorporate self and non-self antigens. Computational experiments using real data on Type-1 Diabetes and Systemic Lupus Erythematosus offer quantitative elucidation of autoimmunity. The results also provide applications towards vaccine design and possible solution to the therapeutic difficulties in the autoimmune diseases and disorders of the above kind.
16
Carbonara, Nunzia. "Competitive Success of Italian Industrial Districts: A Network-based Approach." Journal of Interdisciplinary Economics 30, no. 1 (June 26, 2017): 78–104. http://dx.doi.org/10.1177/0260107917700470.
Full textAbstract:
The article conducts an explorative research on the competitive success of industrial districts (IDs) based on their capacity to adapt and evolve in response to the environmental changes. The aim is to identify the ID structural features supporting adaptation by using the complexity theory. Thus, IDs are considered as complex adaptive systems (CASs) and the ID features that foster adaptation are identified based on the main CAS properties, namely inter-connectivity, heterogeneity and control. To formulate the theory linking the values of the ID structural features with the ID competitive success, a multiple case study is carried out. Finally, three theoretical propositions are derived. JEL: P13, P17, P48, R10
17
Dharmarajan, R., and V. Thiagarasu. "Computation of Risk Severity of the Malicious Node using Adaptive Neuro Fuzzy Inference System (ANFIS)." Asian Journal of Engineering and Applied Technology 8, no. 1 (February 5, 2019): 9–14. http://dx.doi.org/10.51983/ajeat-2019.8.1.1067.
Full textAbstract:
The Intrusion Detection System (IDS) can be employed broadly for safety network. Intrusion Detection Systems (IDSs) are commonly positioned alongside with other protecting safety mechanisms, such as authentication and access control, as a subsequent line of defence that guards data structures. In this paper, Adaptive Neuro Fuzzy Inference System has utilized to predict the risk severity of the malicious nodes found the previous classification phase.
18
Konyeha, Susan, and Emmanuel A. Onibere. "Computer Immunity Using an Intrusion Detection System (IDS)." Advanced Materials Research 824 (September 2013): 200–205. http://dx.doi.org/10.4028/www.scientific.net/amr.824.200.
Full textAbstract:
Computers are involved in every aspect of modern society and have become an essential part of our lives, but their vulnerability is of increasing concern to us. Security flaws are inherent in the operation of computers Most flaws are caused by errors in the process of software engineering or unforeseen mishaps and it is difficult to solve these problems by conventional methods. A radical way of constantly monitoring the system for newly disclosed vulnerabilities is required. In order to devise such a system, this work draws an analogy between computer immune systems and the human immune system. The computer immune system is the equivalent of the human immune system. The primary objective of this paper is to use an intrusion detection system in the design and implementation of a computer immune system that would be built on the framework of the human immune system. This objective is successfully realized and in addition a prevention mechanism using the windows IP Firewall feature has been incorporated. Hence the system is able to perform intrusion detection and prevention. Data was collected about events occurring in a computer network that violate predefined security policy, such as attempts to affect the confidentiality, integrity or its availability using Snort rules for known attacks and adaptive detection for the unknown attacks. The system was tested using real-time data and Intrusion Detection evaluation (IDEVAL) Department of Defense Advanced Research Projects Agency (DARPA) data set. The results were quite encouraging as few false positive were recorded.
19
Sundararajan, Ranjeeth Kumar, and Umamakeswari Arumugam. "Intrusion Detection Algorithm for Mitigating Sinkhole Attack on LEACH Protocol in Wireless Sensor Networks." Journal of Sensors 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/203814.
Full textAbstract:
In wireless sensor network (WSN), the sensors are deployed and placed uniformly to transmit the sensed data to a centralized station periodically. So, the major threat of the WSN network layer is sinkhole attack and it is still being a challenging issue on the sensor networks, where the malicious node attracts the packets from the other normal sensor nodes and drops the packets. Thus, this paper proposes an Intrusion Detection System (IDS) mechanism to detect the intruder in the network which uses Low Energy Adaptive Clustering Hierarchy (LEACH) protocol for its routing operation. In the proposed algorithm, the detection metrics, such as number of packets transmitted and received, are used to compute the intrusion ratio (IR) by the IDS agent. The computed numeric or nonnumeric value represents the normal or malicious activity. As and when the sinkhole attack is captured, the IDS agent alerts the network to stop the data transmission. Thus, it can be a resilient to the vulnerable attack of sinkhole. Above all, the simulation result is shown for the proposed algorithm which is proven to be efficient compared with the existing work, namely, MS-LEACH, in terms of minimum computational complexity and low energy consumption. Moreover, the algorithm was numerically analyzed using TETCOS NETSIM.
20
Apiecionek, Łukasz, and Matusz Biedziak. "Fuzzy Adaptive Data Packets Control Algorithm for IoT System Protection." JUCS - Journal of Universal Computer Science 26, no. 11 (November 28, 2020): 1435–54. http://dx.doi.org/10.3897/jucs.2020.076.
Full textAbstract:
One of huge problem for recent IT systems are attacks on their resources called Distributed Denial of Service attacks. Many servers which are accessible from public network were a victim of such attacks or could be in the future. Unfortunately, there is still no effective method for protecting network servers against source of the attack, while such attack could block network resources for many hours. Existing solutions for protecting networks and IoT systems are using mainly firewalls and IDS/IPS mechanisms, which is not sufficient. This article presents the method minimizing the DDoS attacks. Proposed method provides possibilities for the network administrators to protect their servers and IoT network resources during the attack. The proposed fuzzy adaptive algorithm is using Ordered Fuzzy Numbers for predicting amount of packets which could be passed over the network boarder gateway. Proposed solution will give the opportunity for ordinary users to finish their work when the attack occurs.
21
Alaparthy, Vishwa, and Salvatore D. Morgera. "Modeling an Intrusion Detection System Based on Adaptive Immunology." International Journal of Interdisciplinary Telecommunications and Networking 11, no. 2 (April 2019): 42–55. http://dx.doi.org/10.4018/ijitn.2019040104.
Full textAbstract:
Network security has always has been an area of priority and extensive research. Recent years have seen a considerable growth in experimenting with biologically inspired techniques. This is a consequence of the authors increased understanding of living systems and the application of that understanding to machines and software. The mounting complexity of telecommunications networks and the need for increasing levels of security have been the driving factors. The human body can act as a great role model for its unique abilities in protecting itself from external entities owing to its diverse complexities. Many abnormalities in the human body are similar to that of the attacks in wireless sensor networks (WSN). This article presents the basic ideas that can help modelling a system to counter the attacks on a WSN by monitoring parameters such as energy, frequency of data transfer, data sent and received. This is implemented by exploiting an immune concept called danger theory, which aggregates the anomalies based on the weights of the anomalous parameters. The objective is to design a cooperative intrusion detection system (IDS) based on danger theory.
22
Biggiero, Lucio. "Markets, hierarchies, networks, districts: A cybernetic approach." Human Systems Management 18, no. 2 (July 19, 1999): 71–86. http://dx.doi.org/10.3233/hsm-1999-18203.
Full textAbstract:
IDs are regional hyper-networks that survived the socioeconomic evolution of modern capitalism. They also promise to succeed in the coming post-Fordist development. Italian experience has shown that industrial districts are highly important, perform successfully, and are increasing their survival rates. Beyond definition problems, they are multi-dimensional, complex, and adaptive systems. They can be replicated elsewhere and, regardless of the contingent triggering factors, can grow and change their early imprinting features. Cybernetics offers a sound theoretical basis for understanding the key concepts and redirecting industrial policy interventions.
23
Budiman, Arif, Mohamad Ivan Fanany, and Chan Basaruddin. "Adaptive Online Sequential ELM for Concept Drift Tackling." Computational Intelligence and Neuroscience 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/8091267.
Full textAbstract:
A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem. The scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and hybrid drift. The AOS-ELM also works well for sudden drift and recurrent context change type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using concept drift public data set (SEA and STAGGER) and other public data sets such as MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose taking the rank of the pseudoinverse matrix as an indicator parameter to detect “underfitting” condition.
24
Akremi, Aymen, Hassen Sallay, and Mohsen Rouached. "An Efficient Intrusion Alerts Miner for Forensics Readiness in High Speed Networks." International Journal of Information Security and Privacy 8, no. 1 (January 2014): 62–78. http://dx.doi.org/10.4018/ijisp.2014010104.
Full textAbstract:
Intrusion Detection System is considered as a core tool in the collection of forensically relevant evidentiary data in real or near real time from the network. The emergence of High Speed Network (HSN) and Service oriented architecture/Web Services (SOA/WS) putted the IDS in face of a typical big data management problem. The log files that IDS generates are very enormous making very fastidious and both compute and memory intensive the forensics readiness process. Furthermore the high level rate of wrong alerts complicates the forensics expert alert analysis and it disproves its performance, efficiency and ability to select the best relevant evidences to attribute attacks to criminals. In this context, we propose Alert Miner (AM), an intrusion alert classifier, which classifies efficiently in near real-time the intrusion alerts in HSN for Web services. AM uses an outlier detection technique based on an adaptive deduced association rules set to classify the alerts automatically and without human assistance. AM reduces false positive alerts without losing high sensitivity (up to 95%) and accuracy up to (97%). Therefore AM facilitates the alert analysis process and allows the investigators to focus their analysis on the most critical alerts on near real-time scale and to postpone less critical alerts for an off-line log analysis.
25
Limouchi, Elnaz, and Imad Mahgoub. "Smart Fuzzy Logic-Based Density and Distribution Adaptive Scheme for Efficient Data Dissemination in Vehicular Ad Hoc Networks." Electronics 9, no. 8 (August 12, 2020): 1297. http://dx.doi.org/10.3390/electronics9081297.
Full textAbstract:
In vehicular Ad Hoc Networks (VANETs), smart data dissemination is crucial for efficient exchange of traffic and road information. Given the dynamic nature of VANET, the challenge is to design an adaptive multi-hop broadcast scheme that achieves high reachability while efficiently utilizing the bandwidth by reducing the number of redundant transmissions. In this paper, we propose a novel intelligent fuzzy logic based density and distribution adaptive broadcast protocol for VANETs. The proposed protocol estimates the spatial distribution of vehicles in the network employing the Nearest Neighbor Distance method, and uses it to adapt the transmission range to enhance reachability. To reduce packet collisions, the protocol intelligently adapts the contention window size to the network density and spatial distribution. Bloom filter technique is used to reduce the overhead resulting from the inclusion of the neighbor IDs in the header of the broadcast message, which is needed in identifying the set of potential rebroadcasting vehicles. Our simulation results confirmed the effectiveness of the proposed scheme in enhancing reachability while efficiently utilizing bandwidth.
26
Ni, Tongguang, Xiaoqing Gu, Hongyuan Wang, and Yu Li. "Real-Time Detection of Application-Layer DDoS Attack Using Time Series Analysis." Journal of Control Science and Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/821315.
Full textAbstract:
Distributed denial of service (DDoS) attacks are one of the major threats to the current Internet, and application-layer DDoS attacks utilizing legitimate HTTP requests to overwhelm victim resources are more undetectable. Consequently, neither intrusion detection systems (IDS) nor victim server can detect malicious packets. In this paper, a novel approach to detect application-layer DDoS attack is proposed based on entropy of HTTP GET requests per source IP address (HRPI). By approximating the adaptive autoregressive (AAR) model, the HRPI time series is transformed into a multidimensional vector series. Then, a trained support vector machine (SVM) classifier is applied to identify the attacks. The experiments with several databases are performed and results show that this approach can detect application-layer DDoS attacks effectively.
27
Bahrpeyma, Fouad, Ali Zakerolhoseini, and Hassan Haghighi. "Using IDS fitted Q to develop a real-time adaptive controller for dynamic resource provisioning in Cloud's virtualized environment." Applied Soft Computing 26 (January 2015): 285–98. http://dx.doi.org/10.1016/j.asoc.2014.10.005.
Full text
28
Aldhaheri, Sahar, Daniyal Alghazzawi, Li Cheng, Bander Alzahrani, and Abdullah Al-Barakati. "DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System." Applied Sciences 10, no. 6 (March 11, 2020): 1909. http://dx.doi.org/10.3390/app10061909.
Full textAbstract:
Recently Internet of Things (IoT) attains tremendous popularity, although this promising technology leads to a variety of security obstacles. The conventional solutions do not suit the new dilemmas brought by the IoT ecosystem. Conversely, Artificial Immune Systems (AIS) is intelligent and adaptive systems mimic the human immune system which holds desirable properties for such a dynamic environment and provides an opportunity to improve IoT security. In this work, we develop a novel hybrid Deep Learning and Dendritic Cell Algorithm (DeepDCA) in the context of an Intrusion Detection System (IDS). The framework adopts Dendritic Cell Algorithm (DCA) and Self Normalizing Neural Network (SNN). The aim of this research is to classify IoT intrusion and minimize the false alarm generation. Also, automate and smooth the signal extraction phase which improves the classification performance. The proposed IDS selects the convenient set of features from the IoT-Bot dataset, performs signal categorization using the SNN then use the DCA for classification. The experimentation results show that DeepDCA performed well in detecting the IoT attacks with a high detection rate demonstrating over 98.73% accuracy and low false-positive rate. Also, we compared these results with State-of-the-art techniques, which showed that our model is capable of performing better classification tasks than SVM, NB, KNN, and MLP. We plan to carry out further experiments to verify the framework using a more challenging dataset and make further comparisons with other signal extraction approaches. Also, involve in real-time (online) attack detection.
29
Mittal, Mohit, Rocío Pérez de Prado, Yukiko Kawai, Shinsuke Nakajima, and José E. Muñoz-Expósito. "Machine Learning Techniques for Energy Efficiency and Anomaly Detection in Hybrid Wireless Sensor Networks." Energies 14, no. 11 (May 27, 2021): 3125. http://dx.doi.org/10.3390/en14113125.
Full textAbstract:
Wireless sensor networks (WSNs) are among the most popular wireless technologies for sensor communication purposes nowadays. Usually, WSNs are developed for specific applications, either monitoring purposes or tracking purposes, for indoor or outdoor environments, where limited battery power is a main challenge. To overcome this problem, many routing protocols have been proposed through the last few years. Nevertheless, the extension of the network lifetime in consideration of the sensors capacities remains an open issue. In this paper, to achieve more efficient and reliable protocols according to current application scenarios, two well-known energy efficient protocols, i.e., Low-Energy Adaptive Clustering hierarchy (LEACH) and Energy–Efficient Sensor Routing (EESR), are redesigned considering neural networks. Specifically, to improve results in terms of energy efficiency, a Levenberg–Marquardt neural network (LMNN) is integrated. Furthermore, in order to improve the performance, a sub-cluster LEACH-derived protocol is also proposed. Simulation results show that the Sub-LEACH with LMNN outperformed its competitors in energy efficiency. In addition, the end-to-end delay was evaluated, and Sub-LEACH protocol proved to be the best among existing strategies. Moreover, an intrusion detection system (IDS) has been proposed for anomaly detection based on the support vector machine (SVM) approach for optimal feature selection. Results showed a 96.15% accuracy—again outperforming existing IDS models. Therefore, satisfactory results in terms of energy efficiency, end-to-end delay and anomaly detection analysis were attained.
30
Jesudoss, Sofiya, Auxeeliya Jesudoss, and Ashraph Sulaiman. "An Adaptive Approach to Improve the Accuracy of Packet Pre-Filtering." Advanced Materials Research 367 (October 2011): 241–48. http://dx.doi.org/10.4028/www.scientific.net/amr.367.241.
Full textAbstract:
The current day networks are under deliberate, continuous and premeditated attacks such as Hacker attacks, DoS attacks, IP Address Spoofing, Phishing, Sniffer attacks etc. The Network Intrusion Detection Systems (NIDS) proved to be reliable in parrying most of the issues and challenges faced by the corporate network security systems. But, the NID systems fall short in providing a completely fool-proof network security environment. False negatives and false positives proved to be considerable bottle necks in securing the networks from the attacks. This paper deals with the introduction of a software approach for the packet pre-filtering to ease security threats and the introduction of Network Behavior Analysis to enhance the security of the network. The Network Behavior Analysis helps the system to ease the burdens to the network and security of the network by the false positives. The NIDS compares all the incoming packets with the pre-defined rules or signatures to find suspicious patterns. The pre-filtering approach used in this paper is a result of the observation that very rarely an incoming packet matches the signatures or the IDS rules. During the pre-filtering step, a small portion of the packet is compared against the predefined signatures for any suspicious patterns and the initial pre-filtering match is considered for a full match. For time efficiency, this strategy is compared to more optimistic schemes that allow reassignment of flows between threads, and evaluated using several network packet traces.
31
Ma, Jitao, Xiaohong Chen, Mrinal K. Sen, and Yaru Xue. "Free-surface multiple attenuation for blended data." GEOPHYSICS 81, no. 3 (May 2016): V227—V233. http://dx.doi.org/10.1190/geo2015-0408.1.
Full textAbstract:
Blended data sets are now being acquired because of improved efficiency and reduction in cost compared with conventional seismic data acquisition. We have developed two methods for blended data free-surface multiple attenuation. The first method is based on an extension of surface-related multiple elimination (SRME) theory, in which free-surface multiples of the blended data can be predicted by a multidimensional convolution of the seismic data with the inverse of the blending operator. A least-squares inversion method is used, which indicates that crosstalk noise existed in the prediction result due to the approximate inversion. An adaptive subtraction procedure similar to that used in conventional SRME is then applied to obtain the blended primary — this can damage the energy of primaries. The second method is based on inverse data processing (IDP) theory adapted to blended data. We derived a formula similar to that used in conventional IDP, and we attenuated free-surface multiples by simple muting of the focused points in the inverse data space (IDS). The location of the focused points in the IDS for blended data, which can be calculated, is also related to the blending operator. We chose a singular value decomposition-based inversion algorithm to stabilize the inversion in the IDP method. The advantage of IDP compared with SRME is that, it does not have crosstalk noise and is able to better preserve the primary energy. The outputs of our methods are all blended primaries, and they can be further processed using blended data-based algorithms. Synthetic data examples show that the SRME and IDP algorithms for blended data are successful in attenuating free-surface multiples.
32
Таха Насіф, Нух. "Using statistical traffic analysis to calculate the confidential means of information transmission." Наука і техніка Повітряних Сил Збройних Сил України, no. 1(42,) (January 21, 2021): 118–25. http://dx.doi.org/10.30748/nitps.2021.42.15.
Full textAbstract:
The article considers the modeling of security problems in the Internet as stochastic systems. This allows you to find flaws in existing security systems and offer new solutions. Studying the vulnerabilities of existing security tools can prevent cyber-attacks from taking advantage of weak systems. New, flexible and adaptive security schemes are necessary for emerging security threats elimination. A hybrid network security scheme, including intrusion detection systems and baits, scattered throughout the network is proposed. This combines the advantages of two security technologies. Honeypot is an activity-based network security system that can be a logical addition to the passive detection policies used by the IDS. This integration forces us to balance the safety indicators compared to costs, planning the operation of the device for the proposed system. Formulation of planning problems as a decentralized partially observable Markov decision-making process (DEC-POMDP) allows to make decisions in a distributed manner on each device without the need of centralized management.
33
He, Gao Feng, Tao Zhang, Yuan Yuan Ma, and Xiao Juan Guan. "A Novel and Practical Method for Network Security Situation Prediction." Applied Mechanics and Materials 701-702 (December 2014): 907–10. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.907.
Full textAbstract:
The real-time prediction of network security situation can significantly improve the monitoring and emergency response capability of the network. However, in practice, if there are a large amount of false predictions, the network administrators should become insensitive and will finally ignore all prediction results. In this paper, we try to solve this issue and propose a novel False Positive Adaptive (FPA) method for network security situation prediction. The main idea of our method is using extrainformation to reduce the number of false positives in prediction. In the model training step, we take advantage of host and network information to eliminate meaningless alerts produced by security tools such as Intrusion Detection System (IDS) and firewall, thus assuring the accuracy of the training samples. In the prediction step, we utilize the detection information from security tools to confirm the prediction results automatically. If the previous predictions are not detected, they will be considered as false positives and the prediction model will be retrained by incremental learning. In our work, the model training and incremental learning is accomplished efficiently by neural network and boosting algorithm.
34
Hoang Hai, Tran, Le Huy Hoang, and Eui-nam Huh. "Network Anomaly Detection based on Late Fusion of Several Machine Learning Algorithms." International journal of Computer Networks & Communications 12, no. 6 (November 30, 2020): 117–31. http://dx.doi.org/10.5121/ijcnc.2020.12608.
Full textAbstract:
Today's Internet and enterprise networks are so popular as they can easily provide multimedia and ecommerce services to millions of users over the Internet in our daily lives. Since then, security has been a challenging problem in the Internet's world. That issue is called Cyberwar, in which attackers can aim or raise Distributed Denial of Service (DDoS) to others to take down the operation of enterprises Intranet. Therefore, the need of applying an Intrusion Detection System (IDS) is very important to enterprise networks. In this paper, we propose a smarter solution to detect network anomalies in Cyberwar using Stacking techniques in which we apply three popular machine learning models: k-nearest neighbor algorithm (KNN), Adaptive Boosting (AdaBoost), and Random Decision Forests (RandomForest). Our proposed scheme uses the Logistic Regression method to automatically search for better parameters to the Stacking model. We do the performance evaluation of our proposed scheme on the latest data set NSLKDD 2019 dataset. We also compare the achieved results with individual machine learning models to show that our proposed model achieves much higher accuracy than previous works.
35
Cannavacciuolo, Lorella, Luca Iandoli, Cristina Ponsiglione, and Giuseppe Zollo. "Learning by failure vs learning by habits." International Journal of Entrepreneurial Behavior & Research 23, no. 3 (May 2, 2017): 524–46. http://dx.doi.org/10.1108/ijebr-11-2015-0238.
Full textAbstract:
Purpose The purpose of this paper is to explain the emergence of collaboration networks in entrepreneurial clusters as determined by the way entrepreneurs exchange knowledge and learn through business transactions needed to implement temporary supply chains in networks of co-located firms. Design/methodology/approach A socio-computational approach is adopted to model business transactions and supply chain formation in Marshallian industrial districts (IDs). An agent-based model is presented and used as a virtual lab to test the hypotheses between the firms’ behaviour and the emergence of structural properties at the system level. Findings The simulation findings and their validation based on the comparison with a real world cluster show that the topological properties of the emerging network are influenced by the learning strategies and decision-making criteria firms use when choosing partners. With reference to the specific case of Marshallian IDs it is shown that inertial learning based on history and past collaboration represents in the long term a major impediment for the emergence of hubs and of a network topology that is more conducive to innovation and growth. Research limitations/implications The paper offers an alternative view of entrepreneurial learning (EL) as opposed to the dominant view in which learning occurs as a result of exceptional circumstances (e.g. failure). The results presented in this work show that adaptive, situated, and day-by-day learning has a profound impact on the performance of entrepreneurial clusters. These results are encouraging to motivate additional research in areas such as in modelling learning or in the application of the proposed approach to the analysis of other types of entrepreneurial ecosystems, such as start-up networks and makers’ communities. Practical implications Agent-based model can support policymakers in identifying situated factors that can be leveraged to produce changes at the macro-level through the identification of suitable incentives and social networks re-engineering. Originality/value The paper presents a novel perspective on EL and offers evidence that micro-learning strategies adopted and developed in routine business transactions do have an impact on firms’ performances (survival and growth) as well as on systemic performances related to the creation and diffusion of innovation in firms networks.
36
Zhao, Ruiqin, Yuan Liu, Octavia A. Dobre, Haiyan Wang, and Xiaohong Shen. "An Efficient Topology Discovery Protocol with Node ID Assignment Based on Layered Model for Underwater Acoustic Networks." Sensors 20, no. 22 (November 18, 2020): 6601. http://dx.doi.org/10.3390/s20226601.
Full textAbstract:
Underwater acoustic networks are widely used in survey missions and environmental monitoring. When an underwater acoustic network (UAN) is deployed in a marine region or two UANs merge, each node hardly knows the entire network and may not have a unique node ID. Therefore, a network topology discovery protocol that can complete node discovery, link discovery, and node ID assignment are necessary and important. Considering the limited node energy and long propagation delay in UANs, it is challenging to obtain the network topology with reduced overheads and a short delay in this initial network state. In this paper, an efficient topology discovery protocol (ETDP) is proposed to achieve adaptive node ID assignment and topology discovery simultaneously. To avoiding packet collision in this initial network state, ETDP controls the transmission of topology discovery (TD) packets, based on a local timer, and divides the network into different layers to make nodes transmit TD packets orderly. Exploiting the received TD packets, each node could obtain the network topology and assign its node ID independently. Simulation results show that ETDP completes network topology discovery for all nodes in the network with significantly reduced energy consumption and short delay; meanwhile, it assigns the shortest unique IDs to all nodes with reduced overheads.
37
Sreeja, T., Dr Manna Sheela Rani Chetty, and Sekhar Babu Boddu. "Detecting SQL Injection Using Correlative Log Analysis." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 389. http://dx.doi.org/10.14419/ijet.v7i2.32.15720.
Full textAbstract:
The spiking landscape of cyber-attacks is reflecting its trend towards invoking vulnerabilities in a web application. The vulnerabilities seem to be over-growing second by second beside being over-coming time to time. The reason behind is, new attack vectors are often being deployed by the threat actors. The global cyber security market alone has brought a turnover of about $350 billion, which shows how wide the attack landscape is and how expensive it is to detect, protect and respond to the cyber issues. Most of the security experts have quoted that, the average cost of a data breach will exceed to $150million by 2020 and about 80 percent of the global demography were nowhere aware of such attacks. From the past few years, SQL injection is acting as a major vector in breaching the sensitive data. Detecting SQL injection through log correlation is the most effective methodology utilized under adaptive environments seeking no tool investigation. This paper exposes a detection methodology of an SQL injection attack without any mere concentration on automated tools. The paper goes with a motto of detection through configuring the available resources like web server,database,and an IDS in a way of creating adaptable environment that can bring the entire attacker information through log analysis. The paper would represent the attacker phases in a finite automata.
38
Brand, Charlotte O., Alex Mesoudi, and Thomas J. H. Morgan. "Trusting the experts: The domain-specificity of prestige-biased social learning." PLOS ONE 16, no. 8 (August 11, 2021): e0255346. http://dx.doi.org/10.1371/journal.pone.0255346.
Full textAbstract:
Prestige-biased social learning (henceforth “prestige-bias”) occurs when individuals predominantly choose to learn from a prestigious member of their group, i.e. someone who has gained attention, respect and admiration for their success in some domain. Prestige-bias is proposed as an adaptive social-learning strategy as it provides a short-cut to identifying successful group members, without having to assess each person’s success individually. Previous work has documented prestige-bias and verified that it is used adaptively. However, the domain-specificity and generality of prestige-bias has not yet been explicitly addressed experimentally. By domain-specific prestige-bias we mean that individuals choose to learn from a prestigious model only within the domain of expertise in which the model acquired their prestige. By domain-general prestige-bias we mean that individuals choose to learn from prestigious models in general, regardless of the domain in which their prestige was earned. To distinguish between domain specific and domain general prestige we ran an online experiment (n = 397) in which participants could copy each other to score points on a general-knowledge quiz with varying topics (domains). Prestige in our task was an emergent property of participants’ copying behaviour. We found participants overwhelmingly preferred domain-specific (same topic) prestige cues to domain-general (across topic) prestige cues. However, when only domain-general or cross-domain (different topic) cues were available, participants overwhelmingly favoured domain-general cues. Finally, when given the choice between cross-domain prestige cues and randomly generated Player IDs, participants favoured cross-domain prestige cues. These results suggest participants were sensitive to the source of prestige, and that they preferred domain-specific cues even though these cues were based on fewer samples (being calculated from one topic) than the domain-general cues (being calculated from all topics). We suggest that the extent to which people employ a domain-specific or domain-general prestige-bias may depend on their experience and understanding of the relationships between domains.
39
Nguyen, Anh-Nhat, Van Nhan Vo, Chakchai So-In, and Dac-Binh Ha. "System Performance Analysis for an Energy Harvesting IoT System Using a DF/AF UAV-Enabled Relay with Downlink NOMA under Nakagami-m Fading." Sensors 21, no. 1 (January 4, 2021): 285. http://dx.doi.org/10.3390/s21010285.
Full textAbstract:
This paper investigates system performance in the Internet of Things (IoT) with an energy harvesting (EH) unmanned aerial vehicle (UAV)-enabled relay under Nakagami-m fading, where the time switching (TS) and adaptive power splitting (APS) protocols are applied for the UAV. Our proposed system model consists of a base station (BS), two IoT device (ID) clusters (i.e., a far cluster and a near cluster), and a multiantenna UAV-enabled relay (UR). We adopt a UR-aided TS and APS (U-TSAPS) protocol, in which the UR can dynamically optimize the respective power splitting ratio (PSR) according to the channel conditions. To improve the throughput, the nonorthogonal multiple access (NOMA) technique is applied in the transmission of both hops (i.e., from the BS to the UR and from the UR to the ID clusters). The U-TSAPS protocol is divided into two phases. In the first phase, the BS transmits a signal to the UR. The UR then splits the received signal into two streams for information processing and EH using the APS scheme. In the second phase, the selected antenna of the UR forwards the received signal to the best far ID (BFID) in the far cluster and the best near ID (BNID) in the near cluster using the decode-and-forward (DF) or amplify-and-forward (AF) NOMA scheme. We derive closed-form expressions for the outage probabilities (OPs) at the BFID and BNID with the APS ratio under imperfect channel state information (ICSI) to evaluate the system performance. Based on these derivations, the throughputs of the considered system are also evaluated. Moreover, we propose an algorithm for determining the nearly optimal EH time for the system to minimize the OP. In addition, Monte Carlo simulation results are presented to confirm the accuracy of our analysis based on simulations of the system performance under various system parameters, such as the EH time, the height and position of the UR, the number of UR antennas, and the number of IDs in each cluster.
40
Wang, Yang, Mingqiang Wang, Jingdan Zou, Jin Xu, and Jing Wang. "Provably Secure Identity-Based Encryption and Signature over Cyclotomic Fields." Wireless Communications and Mobile Computing 2019 (October 17, 2019): 1–13. http://dx.doi.org/10.1155/2019/1742386.
Full textAbstract:
Identity-based cryptography is a type of public key cryptography with simple key management procedures. To our knowledge, till now, the existing identity-based cryptography based on NTRU is all over power-of-2 cyclotomic rings. Whether there is provably secure identity-based cryptography over more general fields is still open. In this paper, with the help of the results of collision resistance preimage sampleable functions (CRPSF) over cyclotomic fields, we give concrete constructions of provably secure identity-based encryption schemes (IBE) and identity-based signature schemes (IBS) based on NTRU over any cyclotomic field. Our IBE schemes are provably secure under adaptive chosen-plaintext and adaptive chosen-identity attacks, meanwhile, our IBS schemes are existentially unforgeable against adaptively chosen message and adaptively chosen identity attacks for any probabilistic polynomial time (PPT) adversary in the random oracle model. The securities of both schemes are based on the worst-case approximate shortest independent vectors problem (SIVPγ) over corresponding ideal lattices. The secret key size of our IBE (IBS) scheme is short—only one (two) ring element(s). The ciphertext (signature) is also short—only two (three) ring elements. Meanwhile, as the case of NTRUEncrypt, our IBE scheme could encrypt n bits in each encryption process. These properties may make our schemes have more advantages for some IoT applications over postquantum world in theory.
41
Al-Radaei, Sami A. M., and R. B. Mishra. "A Heuristic Method for Learning Path Sequencing for Intelligent Tutoring System (ITS) in E-learning." International Journal of Intelligent Information Technologies 7, no. 4 (October 2011): 65–80. http://dx.doi.org/10.4018/jiit.2011100104.
Full textAbstract:
Course sequencing is one of the vital aspects in an Intelligent Tutoring System (ITS) for e-learning to generate the dynamic and individual learning path for each learner. Many researchers used different methods like Genetic Algorithm, Artificial Neural Network, and TF-IDF (Term Frequency- Inverse Document Frequency) in E-leaning systems to find the adaptive course sequencing by obtaining the relation between the courseware. In this paper, heuristic semantic values are assigned to the keywords in the courseware based on the importance of the keyword. These values are used to find the relationship between courseware based on the different semantic values in them. The dynamic learning path sequencing is then generated. A comparison is made in two other important methods of course sequencing using TF-IDF and Vector Space Model (VSM) respectively, the method produces more or less same sequencing path in comparison to the two other methods. This method has been implemented using Eclipse IDE for java programming, MySQL as database, and Tomcat as web server.
42
Otoum, Safa, Burak Kantarci, and Hussein Mouftah. "A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures." ACM Transactions on Internet Technology 21, no. 4 (July 22, 2021): 1–22. http://dx.doi.org/10.1145/3406093.
Full textAbstract:
Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic on the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this article, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognise intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KDD’99 as real attack dataset in our simulations. Results present the performance metrics for three different IDSs, namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), and Q-learning based IDS (Q-IDS), to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that Q-IDS performs with detection rate while SARSA-IDS and TD-IDS perform at the order of .
43
Skelboe, Stig. "Adaptive partitioning techniques for index 1 IDEs." BIT Numerical Mathematics 50, no. 2 (February 23, 2010): 405–23. http://dx.doi.org/10.1007/s10543-010-0258-4.
Full text
44
Jeong, Hae-Seong, Gwang-Hoon Baek, and Heung-Gyoon Ryu. "Adaptive Phase Noise Compensator in Wireless ICS Repeater." Journal of Korean Institute of Electromagnetic Engineering and Science 22, no. 4 (April 30, 2011): 481–88. http://dx.doi.org/10.5515/kjkiees.2011.22.4.481.
Full text
45
Ahmadi, Hamid Reza, Navideh Mahdavi, and Mahmoud Bayat. "Applying Adaptive Pushover Analysis to Estimate Incremental Dynamic Analysis Curve." Journal of Earthquake and Tsunami 14, no. 04 (February 27, 2020): 2050016. http://dx.doi.org/10.1142/s1793431120500165.
Full textAbstract:
To estimate seismic demand and capacity of structures, it has been suggested by researchers that Incremental Dynamic Analysis (IDA) is one of the most accurate methods. Although this method shows the most accurate response of the structure, some problems, such as difficulty in modeling, time-consuming analysis and selection of the earthquake records, encourage researchers to find some ways to estimate the dynamic response of structures by using static nonlinear analysis. The simplicity of pushover analysis in evaluating structural nonlinear response serves well as an alternative to the time-history analysis method. In this paper, based on the concepts of the displacement-based adaptive pushover (DAP), a new approach is proposed to estimate the IDA curves. The performance of the proposed method has been investigated using 3- and 9-story moment-resisting frames. In addition, the results were compared with exact IDA curves and IDA curves developed by the modal pushover analysis (MPA) based method. For evaluation, IDA curves with 16%, 50% and 84% fractile were estimated. Using the results, [Formula: see text] capacities corresponding to Collapse Prevention (CP) limit state were calculated and assessed. Finite element modeling of the structures has been carried out by using ZEUS-NL software. Based on the achieved results, the proposed approach can estimate the capacity of the structure accurately. The significant advantage of the applied approach is the low computational cost and desirable accuracy. The proposed approach can be used to develop the approximate IDA curves.
46
Rankin, Lucille, and Gabrielle T. Belz. "Diverse Roles of Inhibitor of Differentiation 2 in Adaptive Immunity." Clinical and Developmental Immunology 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/281569.
Full textAbstract:
The helix-loop-helix (HLH) transcription factor inhibitor of DNA binding 2 (Id2) has been implicated as a regulator of hematopoiesis and embryonic development. While its role in early lymphopoiesis has been well characterized, new roles in adaptive immune responses have recently been uncovered opening exciting new directions for investigation. In the innate immune system, Id2 is required for the development of mature natural killer (NK) cells, lymphoid tissue-inducer (LTi) cells, and the recently identified interleukin (IL)-22 secreting nonconventional innate lymphocytes found in the gut. In addition, Id2 has been implicated in the development of specific dendritic cell (DC) subsets, decisions determining the formation ofαβandγδT-cell development, NK T-cell behaviour, and in the maintenance of effector and memory CD8+T cells in peripheral tissues. Here, we review the current understanding of the role of Id2 in lymphopoiesis and in the development of the adaptive immune response required for maintaining immune homeostasis and immune protection.
47
Jin, Zhujun, Yu Yang, Yuling Chen, and Yuwei Chen. "IAS-CNN: Image adaptive steganalysis via convolutional neural network combined with selection channel." International Journal of Distributed Sensor Networks 16, no. 3 (March 2020): 155014772091100. http://dx.doi.org/10.1177/1550147720911002.
Full textAbstract:
Steganography is conducive to communication security, but the abuse of steganography brings many potential dangers. And then, steganalysis plays an important role in preventing the abuse of steganography. Nowadays, steganalysis based on deep learning generally has a large number of parameters, and its pertinence to adaptive steganography algorithms is weak. In this article, we propose a lightweight convolutional neural network named IAS-CNN which targets to image adaptive steganalysis. To solve the limitation of manually designing residual extraction filters, we adopt the method of self-learning filter in the network. That is, a high-pass filter in spatial rich model is applied to initialize the weights of the first layer and then these weights are updated through the backpropagation of the network. In addition, the knowledge of selection channel is incorporated into IAS-CNN to enhance residuals in regions that have a high probability for steganography by inputting embedding probability maps into IAS-CNN. Also, IAS-CNN is designed as a lightweight network to reduce the consumption of resources and improve the speed of processing. Experimental results show that IAS-CNN performs well in steganalysis. IAS-CNN not only has similar performance with YedroudjNet in S-UNIWARD steganalysis but also has fewer parameters and convolutional computations.
48
Sachindra, Lionel Larribère, Daniel Novak, Huizi Wu, Laura Hüser, Karol Granados, Elias Orouji, and Jochen Utikal. "New role of ID3 in melanoma adaptive drug-resistance." Oncotarget 8, no. 66 (November 27, 2017): 110166–75. http://dx.doi.org/10.18632/oncotarget.22698.
Full text
49
Castillo, Gladys, and João Gama. "Adaptive Bayesian network classifiers." Intelligent Data Analysis 13, no. 1 (February 23, 2009): 39–59. http://dx.doi.org/10.3233/ida-2009-0355.
Full text
50
Naito, Yoshiro, Takeshi Tsujino, Mika Matsumoto, Tsuyoshi Sakoda, Mitsumasa Ohyanagi, and Tohru Masuyama. "Adaptive response of the heart to long-term anemia induced by iron deficiency." American Journal of Physiology-Heart and Circulatory Physiology 296, no. 3 (March 2009): H585—H593. http://dx.doi.org/10.1152/ajpheart.00463.2008.
Full textAbstract:
Anemia is common in patients with chronic heart failure and an independent predictor of poor prognosis. Chronic anemia leads to left ventricular (LV) hypertrophy and heart failure, but its molecular mechanisms remain largely unknown. We investigated the mechanisms, including the molecular signaling pathway, of cardiac remodeling induced by iron deficiency anemia (IDA). Weanling Sprague-Dawley rats were fed an iron-deficient diet for 20 wk to induce IDA, and the molecular mechanisms of cardiac remodeling were evaluated. The iron-deficient diet initially induced severe anemia, which resulted in LV hypertrophy and dilation with preserved systolic function associated with increased serum erythropoietin (Epo) concentration. Cardiac STAT3 phosphorylation and VEGF gene expression increased by 12 wk of IDA, causing angiogenesis in the heart. Thereafter, sustained IDA induced upregulation of cardiac hypoxia inducible factor-1α gene expression and maintained upregulation of cardiac VEGF gene expression and cardiac angiogenesis; however, sustained IDA promoted cardiac fibrosis and lung congestion, with decreased serum Epo concentration and cardiac STAT3 phosphorylation after 20 wk of IDA compared with 12 wk. Upregulation of serum Epo concentration and cardiac STAT3 phosphorylation is associated with a beneficial adaptive mechanism of anemia-induced cardiac hypertrophy, and later decreased levels of these molecules may be critical for the transition from adaptive cardiac hypertrophy to cardiac dysfunction in long-term anemia. Understanding the mechanism of cardiac maladaptation to anemia may lead to a new strategy for treatment of chronic heart failure with anemia.