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Journal articles on the topic 'Adaptive Intrusion Detection System'

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Published: 4 June 2021

Last updated: 1 February 2022

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1

A. M., Riyad, M. S. Irfan Ahmed, and R. L. Raheemaa Khan. "An adaptive distributed Intrusion detection system architecture using multi agents." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (December 1, 2019): 4951. http://dx.doi.org/10.11591/ijece.v9i6.pp4951-4960.

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Intrusion detection systems are used for monitoring the network data, analyze them and find the intrusions if any. The major issues with these systems are the time taken for analysis, transfer of bulk data from one part of the network to another, high false positives and adaptability to the future threats. These issues are addressed here by devising a framework for intrusion detection. Here, various types of co-operating agents are distributed in the network for monitoring, analyzing, detecting and reporting. Analysis and detection agents are the mobile agents which are the primary detection modules for detecting intrusions. Their mobility eliminates the transfer of bulk data for processing. An algorithm named territory is proposed to avoid interference of one analysis agent with another one. A communication layout of the analysis and detection module with other modules is depicted. The inter-agent communication reduces the false positives significantly. It also facilitates the identification of distributed types of attacks. The co-ordinator agents log various events and summarize the activities in its network. It also communicates with co-ordinator agents of other networks. The system is highly scalable by increasing the number of various agents if needed. Centralized processing is avoided here to evade single point of failure. We created a prototype and the experiments done gave very promising results showing the effectiveness of the system.

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2

Simavoryan, Simon Zhorzhevich, Arsen Rafikovich Simonyan, Georgii Aleksandrovich Popov, and Elena Ivanovna Ulitina. "The procedure of intrusions detection in information security systems based on the use of neural networks." Программные системы и вычислительные методы, no. 3 (March 2020): 1–9. http://dx.doi.org/10.7256/2454-0714.2020.3.33734.

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The subject of the research is the problem of identifying and countering intrusions (attacks) in information security systems (ISS) based on the system-conceptual approach, developed within the framework of the RFBR funded project No. 19-01-00383. The object of the research is neural networks and information security systems (ISS) of automated data processing systems (ADPS). The authors proceed from the basic conceptual requirements for intrusion detection systems - adaptability, learnability and manageability. The developed intrusion detection procedure considers both internal and external threats. It consists of two subsystems: a subsystem for detecting possible intrusions, which includes subsystems for predicting, controlling and managing access, analyzing and detecting the recurrence of intrusions, as well as a subsystem for countering intrusions, which includes subsystems for blocking / destroying protected resources, assessing losses associated with intrusions, and eliminating the consequences of the invasion. Methodological studies on the development of intrusion detection procedures are carried out using artificial intelligence methods, system analysis, and the theory of neural systems in the field of information security. Research in this work is carried out on the basis of the achievements of the system-conceptual approach to information security in ADPS.The main result obtained in this work is a block diagram (algorithm) of an adaptive intrusion detection procedure, which contains protection means and mechanisms, built by analogy with neural systems used in security systems.The developed general structure of the intrusion detection and counteraction system allows systematically interconnecting the subsystems for detecting possible intrusions and counteracting intrusions at the conceptual level.

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3

Liu, Yang Bin, Liang Shi, Bei Zhan Wang, Yuan Qin Wu, and Pan Hong Wang. "An New Agent Based Distributed Adaptive Intrusion Detection System." Advanced Materials Research 532-533 (June 2012): 624–29. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.624.

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In order to overcome the excessive dependence among the traditional intrusion detection system components, high rate false-alarm phenomenon caused by multiple alarms to the same invasion, inability to adaptively replace mining algorithm when testing environment has changed and other issues, this paper puts forward an Agent based distributed adaptive intrusion detection system, which employs Joint Detection mechanism for mining algorithm module, and Dynamic Election algorithm for the recovery mechanism, thereby improving the system adaptive ability to the external change.

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4

Yu, Zhenwei, Jeffrey J. P. Tsai, and Thomas Weigert. "An adaptive automatically tuning intrusion detection system." ACM Transactions on Autonomous and Adaptive Systems 3, no. 3 (August 2008): 1–25. http://dx.doi.org/10.1145/1380422.1380425.

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5

P.S., Pawar, and Hashmi S.A. "Security Enhanced Adaptive Acknowledgment Intrusion Detection System." International Journal of Computer Applications 130, no. 7 (November 17, 2015): 51–56. http://dx.doi.org/10.5120/ijca2015907055.

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6

Elfeshawy, Nawal A., and Osama S. Faragallah. "Divided two-part adaptive intrusion detection system." Wireless Networks 19, no. 3 (June 13, 2012): 301–21. http://dx.doi.org/10.1007/s11276-012-0467-7.

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7

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.

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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.

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8

Hacini, Salima, Zahia Guessoum, and Mohamed Cheikh. "False Alarm Reduction Using Adaptive Agent-Based Profiling." International Journal of Information Security and Privacy 7, no. 4 (October 2013): 53–74. http://dx.doi.org/10.4018/ijisp.2013100105.

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In this paper the authors propose a new efficient anomaly-based intrusion detection mechanism based on multi-agent systems. New networks are particularly vulnerable to intrusion, they are often attacked with intelligent and skilful hacking techniques. The intrusion detection techniques have to deal with two problems: intrusion detection and false alarms. The issue of false alarms has an important impact on the success of the anomaly-based intrusion detection technologies. The purpose of this paper is to improve their accuracy by detecting real attacks and by reducing the number of unnecessary generated alerts. The authors' intrusion detection mechanism relies on a set of agents to ensure the detection and the adaptation of normal profile to support the legitimate dynamic changes that occur and are the cause of high rate of false alarms.

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9

Chiche, Alebachew, and Million Meshesha. "Towards a Scalable and Adaptive Learning Approach for Network Intrusion Detection." Journal of Computer Networks and Communications 2021 (January 18, 2021): 1–9. http://dx.doi.org/10.1155/2021/8845540.

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This paper introduces a new integrated learning approach towards developing a new network intrusion detection model that is scalable and adaptive nature of learning. The approach can improve the existing trends and difficulties in intrusion detection. An integrated approach of machine learning with knowledge-based system is proposed for intrusion detection. While machine learning algorithm is used to construct a classifier model, knowledge-based system makes the model scalable and adaptive. It is empirically tested with NSL-KDD dataset of 40,558 total instances, by using ten-fold cross validation. Experimental result shows that 99.91% performance is registered after connection. Interestingly, significant knowledge rich learning for intrusion detection differs as a fundamental feature of intrusion detection and prevention techniques. Therefore, security experts are recommended to integrate intrusion detection in their network and computer systems, not only for well-being of their computer systems but also for the sake of improving their working process.

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10

Owens, Stephen F., and Reuven R. Levary. "An adaptive expert system approach for intrusion detection." International Journal of Security and Networks 1, no. 3/4 (2006): 206. http://dx.doi.org/10.1504/ijsn.2006.011780.

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11

Cheng, Bo-Chao, and Ryh-Yuh Tseng. "A Context Adaptive Intrusion Detection System for MANET." Computer Communications 34, no. 3 (March 2011): 310–18. http://dx.doi.org/10.1016/j.comcom.2010.06.015.

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12

Hofmeyr, Steven A., and Stephanie Forrest. "Architecture for an Artificial Immune System." Evolutionary Computation 8, no. 4 (December 2000): 443–73. http://dx.doi.org/10.1162/106365600568257.

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An artificial immune system (ARTIS) is described which incorporates many properties of natural immune systems, including diversity, distributed computation, error tolerance, dynamic learning and adaptation, and self-monitoring. ARTIS is a general framework for a distributed adaptive system and could, in principle, be applied to many domains. In this paper, ARTIS is applied to computer security in the form of a network intrusion detection system called LISYS. LISYS is described and shown to be effective at detecting intrusions, while maintaining low false positive rates. Finally, similarities and differences between ARTIS and Holland's classifier systems are discussed.

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13

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.

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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.

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14

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.

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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.

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15

Sim, Kwee-Bo, and Jae-Won Yang. "Adaptive Intrusion Detection Algorithm based on Artificial Immune System." Journal of Korean Institute of Intelligent Systems 13, no. 2 (April 1, 2003): 169–74. http://dx.doi.org/10.5391/jkiis.2003.13.2.169.

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16

Lee, Han-Sung, Young-Hee Im, Joo-Young Park, and Dai-Hee Park. "Adaptive Intrusion Detection System Based on SVM and Clustering." Journal of Korean Institute of Intelligent Systems 13, no. 2 (April 1, 2003): 237–42. http://dx.doi.org/10.5391/jkiis.2003.13.2.237.

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17

Platonov, V. V., and P. O. Semenov. "An adaptive model of a distributed intrusion detection system." Automatic Control and Computer Sciences 51, no. 8 (December 2017): 894–98. http://dx.doi.org/10.3103/s0146411617080168.

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18

Priyanka, Lohar, and Lomte Archana. "UNIACK- Universal Adaptive Acknowledge Intrusion Detection System in Manets." International Journal of Computer Applications 123, no. 4 (August 18, 2015): 1–4. http://dx.doi.org/10.5120/ijca2015905276.

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19

Wang, Jin. "An Autonomous Agent-Based Adaptive Distributed Intrusion Detection System." Journal of Computer Research and Development 42, no. 11 (2005): 1934. http://dx.doi.org/10.1360/crad20051116.

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20

Sheltami, Tarek, Abdulsalam Basabaa, and Elhadi Shakshuki. "A3ACKs: adaptive three acknowledgments intrusion detection system for MANETs." Journal of Ambient Intelligence and Humanized Computing 5, no. 4 (June 5, 2014): 611–20. http://dx.doi.org/10.1007/s12652-014-0232-0.

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21

Al-Yaseen, Wathiq Laftah, Zulaiha Ali Othman, and Mohd Zakree Ahmad Nazri. "Real-time multi-agent system for an adaptive intrusion detection system." Pattern Recognition Letters 85 (January 2017): 56–64. http://dx.doi.org/10.1016/j.patrec.2016.11.018.

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22

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.

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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.

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23

Pang, Se-chung, Yang-woo Kim, Yoon-hee Kim, and Phil-Woo Lee. "An Optimum-adaptive Intrusion Detection System Using a Mobile Code." KIPS Transactions:PartC 12C, no. 1 (February 1, 2005): 45–52. http://dx.doi.org/10.3745/kipstc.2005.12c.1.045.

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24

Jagadeesan, A. P., and K. Gnanambal. "Semi-Supervised Multi-Instance Neurologic Adaptive Learning Intrusion Detection System." Applied Mathematics & Information Sciences 13, no. 2 (March 1, 2019): 291–98. http://dx.doi.org/10.18576/amis/130218.

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25

Shafi, Kamran, and Hussein A. Abbass. "An adaptive genetic-based signature learning system for intrusion detection." Expert Systems with Applications 36, no. 10 (December 2009): 12036–43. http://dx.doi.org/10.1016/j.eswa.2009.03.036.

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26

Bakhsh, Sheikh Tahir, Saleh Alghamdi, Rayan A. Alsemmeari, and Syed Raheel Hassan. "An adaptive intrusion detection and prevention system for Internet of Things." International Journal of Distributed Sensor Networks 15, no. 11 (November 2019): 155014771988810. http://dx.doi.org/10.1177/1550147719888109.

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The revolution of computer network technologies and telecommunication technologies increases the number of Internet users enormously around the world. Thus, many companies nowadays produce various devices having network chips, each device becomes part of the Internet of Things and can run on the Internet to achieve various services for its users. This led to the increase in security threats and attacks on these devices. Due to the increased number of devices connected to the Internet, the attackers have more opportunities to perform their attacks in such an environment. Therefore, security has become a big challenge more than before. In addition, confidentiality, integrity, and availability are required components to assure the security of Internet of Things. In this article, an adaptive intrusion detection and prevention system is proposed for Internet of Things (IDPIoT) to enhance security along with the growth of the devices connected to the Internet. The proposed IDPIoT enhances the security including host-based and network-based functionality by examining the existing intrusion detection systems. Once the proposed IDPIoT receives the packet, it examines the behavior, the packet is suspected, and it blocks or drops the packet. The main goal is accomplished by implementing one essential part of security, which is intrusion detection and prevention system.

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27

Zeng, Xia Ling, and Lin Zhang. "Intrusion Detection Model Based on Fuzzy Comprehensive Evaluation." Applied Mechanics and Materials 635-637 (September 2014): 1574–77. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1574.

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An intrusion detection model was designed based on the specific immune classification of human immune system. The intrusion detection module was divided into inherent detection module and adaptive detection module. The inherent detection module inherits currently available rules, and the adaptive detection module proposes an anomaly detection algorithm. The algorithm draws on the theory of fuzzy math, integrates fuzzy comprehensive evaluation with analytic hierarchy, and establishes multi-level fuzzy comprehensive detection model by introducing the concept of fuzzy evaluation tree to improve the accuracy of detection. The results show that the model can accurately detect known attacks and can better detect unknown attacks.

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28

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.

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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.

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29

Glickman, Matthew, Justin Balthrop, and Stephanie Forrest. "A Machine Learning Evaluation of an Artificial Immune System." Evolutionary Computation 13, no. 2 (June 2005): 179–212. http://dx.doi.org/10.1162/1063656054088503.

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ARTIS is an artificial immune system framework which contains several adaptive mechanisms. LISYS is a version of ARTIS specialized for the problem of network intrusion detection. The adaptive mechanisms of LISYS are characterized in terms of their machine-learning counterparts, and a series of experiments is described, each of which isolates a different mechanism of LISYS and studies its contribution to the system's overall performance. The experiments were conducted on a new data set, which is more recent and realistic than earlier data sets. The network intrusion detection problem is challenging because it requires one-class learning in an on-line setting with concept drift. The experiments confirm earlier experimental results with LISYS, and they study in detail how LISYS achieves success on the new data set.

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30

Wang, Yang, Liqiang Zhu, Zujun Yu, and Baoqing Guo. "An Adaptive Track Segmentation Algorithm for a Railway Intrusion Detection System." Sensors 19, no. 11 (June 6, 2019): 2594. http://dx.doi.org/10.3390/s19112594.

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Video surveillance-based intrusion detection has been widely used in modern railway systems. Objects inside the alarm region, or the track area, can be detected by image processing algorithms. With the increasing number of surveillance cameras, manual labeling of alarm regions for each camera has become time-consuming and is sometimes not feasible at all, especially for pan-tilt-zoom (PTZ) cameras which may change their monitoring area at any time. To automatically label the track area for all cameras, video surveillance system requires an accurate track segmentation algorithm with small memory footprint and short inference delay. In this paper, we propose an adaptive segmentation algorithm to delineate the boundary of the track area with very light computation burden. The proposed algorithm includes three steps. Firstly, the image is segmented into fragmented regions. To reduce the redundant calculation in the evaluation of the boundary weight for generating the fragmented regions, an optimal set of Gaussian kernels with adaptive directions for each specific scene is calculated using Hough transformation. Secondly, the fragmented regions are combined into local areas by using a new clustering rule, based on the region’s boundary weight and size. Finally, a classification network is used to recognize the track area among all local areas. To achieve a fast and accurate classification, a simplified CNN network is designed by using pre-trained convolution kernels and a loss function that can enhance the diversity of the feature maps. Experimental results show that the proposed method finds an effective balance between the segmentation precision, calculation time, and hardware cost of the system.

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31

Resende, Paulo Angelo Alves, and André Costa Drummond. "Adaptive anomaly-based intrusion detection system using genetic algorithm and profiling." Security and Privacy 1, no. 4 (July 2018): e36. http://dx.doi.org/10.1002/spy2.36.

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32

Orang, Zahra Atashbar, Ezzat Moradpour, Ahmad Habibizad Navin, Amir Azimi Alasti Ahrabim, and Mir Kamal Mirnia. "Using Adaptive Neuro-Fuzzy Inference System in Alert Management of Intrusion Detection Systems." International Journal of Computer Network and Information Security 4, no. 11 (October 9, 2012): 32–38. http://dx.doi.org/10.5815/ijcnis.2012.11.04.

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33

Venkatraman, S., and B. Surendiran. "Adaptive hybrid intrusion detection system for crowd sourced multimedia internet of things systems." Multimedia Tools and Applications 79, no. 5-6 (May 4, 2019): 3993–4010. http://dx.doi.org/10.1007/s11042-019-7495-6.

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34

Teng, Shaohua, Naiqi Wu, Haibin Zhu, Luyao Teng, and Wei Zhang. "SVM-DT-based adaptive and collaborative intrusion detection." IEEE/CAA Journal of Automatica Sinica 5, no. 1 (January 2018): 108–18. http://dx.doi.org/10.1109/jas.2017.7510730.

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35

Et. al., K. NandhaKumar,. "A Hybrid Adaptive Development Algorithm and Machine Learning Based Method for Intrusion Detection and Prevention System." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (April 11, 2021): 1226–36. http://dx.doi.org/10.17762/turcomat.v12i5.1789.

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Network Intrusion detection and prevention Systems (NIDPS) are employed in monitoring a network which safeguards user integrity, privacy thereby ensuring the data security and availability in a network. Such systems not only monitor the suspicious activities in a network but also used as control systems to eliminate the malicious users from the network. In this paper, a Hybrid Adaptive Development Algorithm and Machine Learning Algorithm (ADA-MLA) method is proposed to identify the malicious activities and eliminating them from the network. The deployment of honeypot-based intrusion is improved adaptive development algorithm. Machine learning algorithm has been employed in the Hybrid IDPS for learning the network data patterns which also identifies the maximum probable attacks in the network. The signatures for the DARPA 99 data set have been updated during the implementation of intrusion prevention system on a real-time basis. The hybrid method works on (i) classifying the attacks based on protocols and (ii) classifying the attacks on pre-determined threshold values. Hence, both known and unknown attacks can be easily captured in the proposed hybrid IDPS method which thereby achieves higher attack detection and prevention accuracy while compared to the conventional attack detection and prevention methodologies.

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36

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.

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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.

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37

LONG, JUN, WENTAO ZHAO, FANGZHOU ZHU, and ZHIPING CAI. "ACTIVE LEARNING TO DEFEND POISONING ATTACK AGAINST SEMI-SUPERVISED INTRUSION DETECTION CLASSIFIER." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 19, supp01 (December 2011): 93–106. http://dx.doi.org/10.1142/s0218488511007362.

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Intrusion detection systems play an important role in computer security. To make intrusion detection systems adaptive to changing environments, supervised learning techniques had been applied in intrusion detection. However, supervised learning needs a large amount of training instances to obtain classifiers with high accuracy. Limited to lack of high quality labeled instances, some researchers focused on semi-supervised learning to utilize unlabeled instances enhancing classification. But involving the unlabeled instances into the learning process also introduces vulnerability: attackers can generate fake unlabeled instances to mislead the final classifier so that a few intrusions can not be detected. In this paper we show that the attacker could mislead the semi-supervised intrusion detection classifier by poisoning the unlabeled instances. And we propose a defend method based on active learning to defeat the poisoning attack. Experiments show that the poisoning attack can reduce the accuracy of the semi-supervised learning classifier and the proposed defending method based on active learning can obtain higher accuracy than the original semi-supervised learner under the presented poisoning attack.

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38

Luo, Xiaolin. "Model design artificial intelligence and research of adaptive network intrusion detection and defense system using fuzzy logic." Journal of Intelligent & Fuzzy Systems 40, no. 4 (April 12, 2021): 8227–35. http://dx.doi.org/10.3233/jifs-189645.

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Along with improvement of technology in network and continuous expansion of network economy and network applications, the Internet has gradually become an indispensable part of the modern society. However, an endless stream of hacker attacks and network virus events make network security issues stand out. Therefore, network security has become a hot spot in computer network research and development. This paper aims at establishing a real-time detection and dynamic defense security system and makes an in-depth study of intrusion detection technology and defense decision-making technology. The strategy involved in finding the intrusion behavior since the fuzzy base contains the better group of rules. We have utilized an automated fuzzy rule generation strategy. An adaptive network intrusion detection and defense system model is established, and the architecture of the model is discussed in detail. The platform independence, good self-adaptability, expansibility, multi-level data analysis and dynamic defense decision-making are expounded. The experiment proves that the model proposed in this article has a good self-adaptability and open construction, and effectively combines the functions of intrusion detection and defense decision-making.

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39

Wan, Lin. "V-Detector-Based Immune Negative Intrusion Detection Algorithm." Applied Mechanics and Materials 263-266 (December 2012): 2966–71. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2966.

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Research the problem of intrusion detection in network security. For the defects that existing intrusion detection system can't recognize unknown attacks, to improve the detection efficiency and reduce false alarm rate, this paper basing on V-detector immunity negative intrusion detection algorithm, bring up an adaptive variable radius detector. In the training stage, randomly generate candidate detector with different detection threshold. In the testing stage, according to the size of hole and detection accuracy automatically adjust the radius of the detector. The experiment and the data of KDDCUP1999 show: under the circumstance of the same number of detector, compared with V-detector, this detector has higher coverage, less hole.

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40

Cai, Yu. "Mobile Agent Based Network Defense System in Enterprise Network." International Journal of Handheld Computing Research 2, no. 1 (January 2011): 41–54. http://dx.doi.org/10.4018/jhcr.2011010103.

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Security has become the Achilles’ heel of many organizations in today’s computer-dominated society. In this paper, a configurable intrusion detection and response framework named Mobile Agents based Distributed (MAD) security system was proposed for enterprise network consisting of a large number of mobile and handheld devices. The key idea of MAD is to use autonomous mobile agents as lightweight entities to provide unified interfaces for intrusion detection, intrusion response, information fusion, and dynamic reconfiguration. These lightweight agents can be easily installed and managed on mobile and handheld devices. The MAD framework includes a family of autonomous agents, servers and software modules. An Object-based intrusion modeling language (mLanguage) is proposed to allow easy data sharing and system control. A data fusion engine (mEngine) is used to provide fused results for traffic classification and intrusion identification. To ensure Quality-of-Service (QoS) requirements for end users, adaptive resource allocation scheme is also presented. It is hoped that this project will advance the understanding of complex, interactive, and collaborative distributed systems.

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Liu, Xuefei, Chao Zhang, Pingzeng Liu, Maoling Yan, Baojia Wang, Jianyong Zhang, and Russell Higgs. "Application of Temperature Prediction Based on Neural Network in Intrusion Detection of IoT." Security and Communication Networks 2018 (December 18, 2018): 1–10. http://dx.doi.org/10.1155/2018/1635081.

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The security of network information in the Internet of Things faces enormous challenges. The traditional security defense mechanism is passive and certain loopholes. Intrusion detection can carry out network security monitoring and take corresponding measures actively. The neural network-based intrusion detection technology has specific adaptive capabilities, which can adapt to complex network environments and provide high intrusion detection rate. For the sake of solving the problem that the farmland Internet of Things is very vulnerable to invasion, we use a neural network to construct the farmland Internet of Things intrusion detection system to detect anomalous intrusion. In this study, the temperature of the IoT acquisition system is taken as the research object. It has divided which into different time granularities for feature analysis. We provide the detection standard for the data training detection module by comparing the traditional ARIMA and neural network methods. Its results show that the information on the temperature series is abundant. In addition, the neural network can predict the temperature sequence of varying time granularities better and ensure a small prediction error. It provides the testing standard for the construction of an intrusion detection system of the Internet of Things.

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Zhuang, Wei, Yixian Shen, Lu Li, Chunming Gao, and Dong Dai. "Develop an Adaptive Real-Time Indoor Intrusion Detection System Based on Empirical Analysis of OFDM Subcarriers." Sensors 21, no. 7 (March 25, 2021): 2287. http://dx.doi.org/10.3390/s21072287.

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Device-free passive intrusion detection is a promising technology to determine whether moving subjects are present without deploying any specific sensors or devices in the area of interest. With the rapid development of wireless technology, multi-input multi-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) which were originally exploited to improve the stability and bandwidth of Wi-Fi communication, can now support extensive applications such as indoor intrusion detection, patient monitoring, and healthcare monitoring for the elderly. At present, most research works use channel state information (CSI) in the IEEE 802.11n standard to analyze signals and select features. However, there are very limited studies on intrusion detection in real home environments that consider scenarios that include different motion speeds, different numbers of intruders, varying locations of devices, and whether people are present sleeping at home. In this paper, we propose an adaptive real-time indoor intrusion detection system using subcarrier correlation-based features based on the characteristics of narrow frequency spacing of adjacent subcarriers. We propose a link-pair selection algorithm for choosing an optimal link pair as a baseline for subsequent CSI processing. We prototype our system on commercial Wi-Fi devices and compare the overall performance with those of state-of-the-art approaches. The experimental results demonstrate that our system achieves impressive performance regardless of intruder’s motion speeds, number of intruders, non-line-of-sight conditions, and sleeping occupant conditions.

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Choi, Seul-Gi, and Sung-Bae Cho. "Evolutionary Reinforcement Learning for Adaptively Detecting Database Intrusions." Logic Journal of the IGPL 28, no. 4 (December 9, 2019): 449–60. http://dx.doi.org/10.1093/jigpal/jzz053.

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Abstract Relational database management system (RDBMS) is the most popular database system. It is important to maintain data security from information leakage and data corruption. RDBMS can be attacked by an outsider or an insider. It is difficult to detect an insider attack because its patterns are constantly changing and evolving. In this paper, we propose an adaptive database intrusion detection system that can be resistant to potential insider misuse using evolutionary reinforcement learning, which combines reinforcement learning and evolutionary learning. The model consists of two neural networks, an evaluation network and an action network. The action network detects the intrusion, and the evaluation network provides feedback to the detection of the action network. Evolutionary learning is effective for dynamic patterns and atypical patterns, and reinforcement learning enables online learning. Experimental results show that the performance for detecting abnormal queries improves as the proposed model learns the intrusion adaptively using Transaction Processing performance Council-E scenario-based virtual query data. The proposed method achieves the highest performance at 94.86%, and we demonstrate the usefulness of the proposed method by performing 5-fold cross-validation.

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Anjana Devi and Bhuvaneswaran. "Adaptive Association Rule Mining Based Cross Layer Intrusion Detection System for MANET." International Journal of Network Security & Its Applications 3, no. 5 (September 30, 2011): 243–56. http://dx.doi.org/10.5121/ijnsa.2011.3519.

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Ananthi, P., and P. Balasubramanie. "An Adaptive Hybrid Multi-level Intelligent Intrusion Detection System for Network Security." Research Journal of Applied Sciences, Engineering and Technology 7, no. 16 (April 25, 2014): 3348–55. http://dx.doi.org/10.19026/rjaset.7.680.

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Liu, Jingmei, Yuanbo Gao, and Fengjie Hu. "A fast network intrusion detection system using adaptive synthetic oversampling and LightGBM." Computers & Security 106 (July 2021): 102289. http://dx.doi.org/10.1016/j.cose.2021.102289.

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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.

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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.

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Korba, Abdelaziz Amara, Mehdi Nafaa, and Salim Ghanemi. "Hybrid Intrusion Detection Framework for Ad hoc networks." International Journal of Information Security and Privacy 10, no. 4 (October 2016): 1–32. http://dx.doi.org/10.4018/ijisp.2016100101.

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In this paper, a cluster-based hybrid security framework called HSFA for ad hoc networks is proposed and evaluated. The proposed security framework combines both specification and anomaly detection techniques to efficiently detect and prevent wide range of routing attacks. In the proposed hierarchical architecture, cluster nodes run a host specification-based intrusion detection system to detect specification violations attacks such as fabrication, replay, etc. While the cluster heads run an anomaly-based intrusion detection system to detect wormhole and rushing attacks. The proposed specification-based detection approach relies on a set of specifications automatically generated, while anomaly-detection uses statistical techniques. The proposed security framework provides an adaptive response against attacks to prevent damage to the network. The security framework is evaluated by simulation in presence of malicious nodes that can launch different attacks. Simulation results show that the proposed hybrid security framework performs significantly better than other existing mechanisms.

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Boudjemaa, Kheddar, and Ping Song. "Indoor Human Detection and Monitoring System Using PIR Wireless Sensors Array." Applied Mechanics and Materials 541-542 (March 2014): 1297–303. http://dx.doi.org/10.4028/www.scientific.net/amm.541-542.1297.

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This paper presents a design and implementation of an efficient and low cost system for indoor monitoring of human intrusion. The system design is based on the use of already available pyroelectric infrared passive sensors (PIR) that are able to detect thermal perturbation caused by moving objects within their field of view (FOV). Our design uses the PIR sensors in the geometric context as binary detectors with adaptive threshold estimation. The combined field of view of three PIR detectors is modulated by a custom designed lens mask to estimate the bearing angle of the single human intrusion. The prototype is formed by a sensing module routed wirelessly to another host module to visualize processed raw data.

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Zou, Li Kun, Shao Kun Liu, and Guo Fu Ma. "Intrusion Detection Model Based on Improved Genetic Algorithm Neural Network in Computer Integrated Process System." Applied Mechanics and Materials 380-384 (August 2013): 2708–11. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2708.

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In order to solve the problems of high false alarm rate and fail rate in intrusion detection system of Computer Integrated Process System (CIPS) network, this paper takes advantage that Genetic Algorithm (GA) possesses overall optimization seeking ability and neural network has formidable approaching ability to the non-linear mapping to propose an intrusion detection model based on Genetic Algorithm Neural Network (GANN) with self-learning and adaptive capacity, which includes data collection module, data preprocessing module, neural network analysis module and intrusion alarm module. To overcome the shortcomings that GA is easy to fall into the extreme value and searches slowly, it improves the adjusting method of GANN fitness value and optimizes the parameter settings of GA. The improved GA is used to optimize BP neural network. Simulation results show that the model makes the detection rate of the system enhance to 97.11%.

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