Tytuł artykułu
Autorzy
Treść / Zawartość
Pełne teksty:
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Multi-Agent Systems (MAS) have been widely used in many areas like modeling and simulation of complex phenomena, and distributed problem solving. Likewise, MAS have been used in cyber-security, to build more efficient Intrusion Detection Systems (IDS), namely Collaborative Intrusion Detection Systems (CIDS). This work presents a taxonomy for classifying the methods used to design intrusion detection systems, and how such methods were used alongside with MAS in order to build IDS that are deployed in distributed environments, resulting in the emergence of CIDS. The proposed taxonomy, consists of three parts: 1) general architecture of CIDS, 2) the used agent technology, and 3) decision techniques, in which used technologies are presented. The proposed taxonomy reviews and classifies the most relevant works in this topic and highlights open research issues in view of recent and emerging threats. Thus, this work provides a good insight regarding past, current, and future solutions for CIDS, and helps both researchers and professionals design more effective solutions.
Słowa kluczowe
Wydawca
Rocznik
Tom
Strony
111--142
Opis fizyczny
Bibliogr. 202 poz., rys.
Twórcy
autor
- Department of Computer Science, 20 August 1955 University of Skikda, Algeria
autor
- Department of Computer Science, 20 August 1955 University of Skikda, Algeria
autor
- Department of Computer Science, 20 August 1955 University of Skikda, Algeria
autor
- Department of Computer Science, 20 August 1955 University of Skikda, Algeria
autor
- Center of Excellence in Information Assurance (COEIA), King Saud University, Riyadh, Saudi Arabia
autor
- Department of Industrial Engineering, Alfaisal University, Riyadh 12714, Saudi Arabia
Bibliografia
- [1] F. Abdoli and M. Kahani. Ontology-based distributed intrusion detection system. In 2009 14th International CSI Computer Conference, pages 65–70. IEEE, oct 2009.
- [2] Yuehui. ABRAHAM, Ajith; GROSAN, Crina; et CHEN. Cyber security and the evolution in intrusion detection systems. Journal of Engineering and Technology, pages 0973–2632, 2005.
- [3] Abdulla Amin Aburomman and Mamun Bin Ibne Reaz. Survey of learning methods in intrusion detection systems. In 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering (ICAEES), pages 362–365. IEEE, nov 2016.
- [4] Omar Achbarou, My Ahmed El Kiram, Outmane Bourkoukou, and Salim Elbouanani. A New Distributed Intrusion Detection System Based on Multi-Agent System for Cloud Environment. International Journal of Communication Networks and Information Security (IJCNIS), 10(3):2018, 2018.
- [5] Neda Afzali Seresht and Reza Azmi. MAISIDS: A distributed intrusion detection system using multi-agent AIS approach. Engineering Applications of Artificial Intelligence, 35:286–298, oct 2014.
- [6] Mohssine El Ajjouri, Siham Benhadou, and Hicham Medromi. New collaborative intrusion detection architecture based on multi agent systems. In 2015 International Conference on Wireless Networks and Mobile Communications (WINCOM), pages 1–6. IEEE, oct 2015.
- [7] A. Sima. AKYAZI, Ugur et UYAR. Distributed detection of DDoS attacks during the intermediate phase through mobile agents. Computing and Informatics, 31(4):759–778, 2012.
- [8] Arwa Aldweesh, Abdelouahid Derhab, and Ahmed Z Emam. Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues. Knowledge-Based Systems, 189:105124, 2020.
- [9] Md. Zahangir Alom, VenkataRamesh Bontupalli, and Tarek M. Taha. Intrusion detection using deep belief networks. In 2015 National Aerospace and Electronics Conference (NAECON), pages 339–344. IEEE, jun 2015.
- [10] Dinesha Hagare Annappaian and Vinod Kumar Agrawal. Cloud Services Usage Profile Based Intruder Detection and Prevention System: Intrusion Meter. Transactions on Networks and Communications, 2(6):12–24, dec 2014.
- [11] A.F. Atiya, S.M. El-Shoura, S.I. Shaheen, and M.S. El-Sherif. A comparison between neural-network forecasting techniques-case study: river flow forecasting. IEEE Transactions on Neural Networks, 10(2):402–409, mar 1999.
- [12] A.B. Badiru. Computational survey of univariate and multivariate learning curve models. IEEE Transactions on Engineering Management, 39(2):176–188, may 1992.
- [13] Daniel Barbara, Ningning Wu, and Sushil Jajodia. Detecting novel network intrusions using bayes estimators. In Proceedings of the 2001 SIAM International Conference on Data Mining, pages 1–17. SIAM, 2001.
- [14] Zahra Beheshti and Siti Mariyam Hj Shamsuddin. A review of population-based meta-heuristic algorithms. Int. J. Adv. Soft Comput. Appl, 5(1):1–35, 2013.
- [15] Mohamed Belaoued, Abdelaziz Boukellal, Mohamed Amir Koalal, Abdelouahid Derhab, Smaine Mazouzi, and Farrukh Aslam Khan. Combined dynamic multi-feature and rule-based behavior for accurate malware detection. International Journal of Distributed Sensor Networks, 15(11):155014771988990, nov 2019.
- [16] Mohamed Belaoued, Abdelouahid Derhab, Smaine Mazouzi, and Farrukh Aslam Khan. MACoMal: A Multi-Agent Based Collaborative Mechanism for Anti-Malware Assistance. IEEE Access, 8:14329–14343, 2020.
- [17] Mohamed Belaoued, Bouchra Guelib, Yasmine Bounaas, Abdelouahid Derhab, and Mahmoud Boufaida. Malware detection system based on an indepth analysis of the portable executable headers. In International conference on machine learning for networking, pages 166–180. Springer, 2018.
- [18] Y. Bengio. Learning Deep Architectures for AI. Foundations and Trends® in Machine Learning, 2(1):1–127, 2009.
- [19] Y. Bengio, A. Courville, and P. Vincent. Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1798–1828, aug 2013.
- [20] Vladimir Bobor. Efficient Intrusion Detection System Architecture Based on Neural Networks and Genetic Algorithms. Department of Computer and Systems Sciences, Stockholm University/Royal Institute of Technology, KTH/DSV, 2006.
- [21] Sven-Erik Bornscheuer. Integrating reactive and reflective reasoning by generating rational models. pages 83–94. 1998.
- [22] Bernhard E. Boser, Isabelle M. Guyon, and Vladimir N. Vapnik. A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory - COLT ’92, pages 144–152, New York, New York, USA, 1992. ACM Press.
- [23] K. Boudaoud, H. Labiod, R. Boutaba, and Z. Guessoum. Network security management with intelligent agents. In IEEE Symposium Record on Network Operations and Management Symposium, pages 579–592. IEEE, 2000.
- [24] Imen Brahmi and Hanen Brahmi. OMAIDS: A Multi-agents Intrusion Detection System Based Ontology. pages 156–163. 2015.
- [25] Imen Brahmi, Sadok Ben Yahia, Hamed Aouadi, and Pascal Poncelet. Towards a multiagent-based distributed intrusion detection system using data mining approaches. In International Workshop on Agents and Data Mining Interaction, pages 173–194. Springer, 2011.
- [26] Krupa Brahmkstri, Devasia Thomas, S. T. Sawant, Avdhoot Jadhav, and D. D. Kshirsagar. Ontology Based Multi-Agent Intrusion Detection System for Web Service Attacks Using Self Learning. pages 265–274. 2014.
- [27] D Brickley and R V Guha. Rdfs: Resource description framework schema. W3C Working Draft, 12, 2002.
- [28] Vladimir Bukhtoyarov and Vadim Zhukov. Ensemble-Distributed Approach in Classification Problem Solution for Intrusion Detection Systems. pages 255–265. 2014.
- [29] Dusan Bulatovic and Dusan Velasevic. A Distributed Intrusion Detection System Based on Bayesian Alarm Networks. pages 219–228. 1999.
- [30] Dusan Bulatovic and Dusan Velasevic. A distributed intrusion detection system based on bayesian alarm networks. In International Exhibition and Congress on Network Security, pages 219–228. Springer, 1999.
- [31] Alexander Bystritsky, Deborah L. Ackerman, Richard M. Rosen, Tanya Vapnik, Eda Gorbis, Karron M. Maidment, and Sanjaya Saxena. Augmentation of Serotonin Reuptake Inhibitors in Refractory Obsessive-Compulsive Disorder Using Adjunctive Olanzapine. The Journal of Clinical Psychiatry, 65(4):565–568, apr 2004.
- [32] James Cannady, Jay Harrell, et al. A comparative analysis of current intrusion detection technologies. In Proceedings of the Fourth Technology for Information Security Conference, volume 96, 1996.
- [33] James D. Cannady. Artificial neural networks for misuse detection. In Proceedings of the 21st National information systems security conference, volume 26, pages 368–381. Baltimore, 1998.
- [34] Brian Caswell and Jay Beale. Snort 2.1 intrusion detection. Elsevier, 2004.
- [35] Tsung Han Chan, Kui Jia, Shenghua Gao, Jiwen Lu, Zinan Zeng, and Yi Ma. PCANet: A Simple Deep Learning Baseline for Image Classification? IEEE Transactions on Image Processing, 24(12):5017–5032, 2015.
- [36] Jennifer A. CHANDLER. Security in cyberspace: combatting distributed denial of service attacks. U. Ottawa L. & Tech. J., 1, 2003.
- [37] RUCHI CHATURVEDI, BABITA PATHIK, and SHIV KUMAR. Intrusion Detection Using Data Mining Along Fuzzy Logic & Genetic Algorithms. Journal of Computer and Information Technology, 09(01):9–13, 2018.
- [38] Ping Chen, Lieven Desmet, and Christophe Huygens. A study on advanced persistent threats. In IFIP International Conference on Communications and Multimedia Security, pages 63–72. Springer, 2014.
- [39] Wun-Hwa Chen, Sheng-Hsun Hsu, and Hwang-Pin Shen. Application of SVM and ANN for intrusion detection. Computers & Operations Research, 32(10):2617–2634, oct 2005.
- [40] T. Chheda, T. Mukerji, A.H. Scheirer, and S.A. Graham. Bayesian Networks for Decisions under Uncertainty in Basin Modeling. jun 2018.
- [41] Crispin Cowan, F Wagle, Calton Pu, Steve Beattie, and Jonathan Walpole. Buffer overflows: Attacks and defenses for the vulnerability of the decade. In Proceedings DARPA Information Survivability Conference and Exposition. DISCEX’00, volume 2, pages 119–129. IEEE, 2000.
- [42] Mark Crosbie and Eugene H Spafford. Applying Genetic Programming to Intrusion Detection. Working Notes for the AAAI Symposium on Genetic Programming, pages 1–8, 1995.
- [43] Fatemeh Daneshfar and Hassan Bevrani. Multiagent systems in control engineering: a survey. Journal of Control Science and Engineering, 2009, 2009.
- [44] Amin Dastanpour, Suhaimi Ibrahim, Reza Mashinchi, and Ali Selamat. Comparison of genetic algorithm optimization on artificial neural network and support vector machine in intrusion detection system. In 2014 IEEE Conference on Open Systems (ICOS), pages 72–77. IEEE, oct 2014.
- [45] M. de Boer, Pieter; Pels. Host-based Intrusion Detection Systems. Retrieved from. 2005.
- [46] Dorothy Denning and Peter G Neumann. Requirements and model for IDES-a real-time intrusion-detection expert system, volume 8. SRI International, 1985.
- [47] Marco Dorigo, Vittorio Maniezzo, and Alberto Colorni. Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 26(1):29–41, 1996.
- [48] Ali Dorri, Salil S Kanhere, and Raja Jurdak. Multiagent systems: A survey. IEEE Access, 6:28573–28593, 2018.
- [49] Wesley M Eddy. Defenses against tcp syn flooding attacks. The Internet Protocol Journal, 9(4):2–16, 2006.
- [50] Adel S. Eesa, Adnan M. Abdulazeez, and Zeynep Orman. A DIDS Based on The Combination of Cuttlefish Algorithm and Decision Tree. Science Journal of University of Zakho, 5(4):313, dec 2017.
- [51] Mohamad. EID. A new mobile agent-based intrusion detection system using distributed sensors. proceeding of FEASC, 2004.
- [52] Mohamed El Bekri and Ouafaa Diouri. Pso based intrusion detection: A pre-implementation discussion. Procedia Computer Science, 160:837–842, 2019.
- [53] Charles Elkan. Results of the KDD’99 classifier learning. ACM SIGKDD Explorations Newsletter, 1(2):63, jan 2000.
- [54] W. Fan, M. Miller, S. Stolfo, W. Lee, and P. Chan. Using artificial anomalies to detect unknown and known network intrusions. Knowledge and Information Systems, 6(5):507–527, sep 2004.
- [55] J. Doyne Farmer, Norman H. Packard, and Alan S. Perelson. The immune system, adaptation, and machine learning. Physica D: Nonlinear Phenomena, 22(1-3):187–204, 1986.
- [56] Jacques Ferber and Gerhard Weiss. Multi-agent systems: an introduction to distributed artificial intelligence, volume 1. Addison-Wesley Reading, 1999.
- [57] Mohamed Amine Ferrag, Leandros Maglaras, Sotiris Moschoyiannis, and Helge Janicke. Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications, 50:102419, 2020.
- [58] E.A. Fisch, G.B. White, and U.W. Pooch. The design of an audit trail analysis tool. In Tenth Annual Computer Security Applications Conference, pages 126–132. IEEE Comput. Soc. Press, 1994.
- [59] Gianluigi Folino and Pietro Sabatino. Ensemble based collaborative and distributed intrusion detection systems: A survey. Journal of Network and Computer Applications, 66:1–16, 2016.
- [60] Gianluigi Folino and Pietro Sabatino. Ensemble based collaborative and distributed intrusion detection systems: A survey. Journal of Network and Computer Applications, 66:1–16, may 2016.
- [61] Kevin L Fox, Ronda R Henning, Jonathan H Reed, and Richard P Simonian. A neural network approach towards intrusion detection. Proceedings of the 13th National Computer Security Conference, 1:125–134, 1990.
- [62] Stefan Fünfrocken. Transparent migration of java-based mobile agents: Capturing and re-establishing the state of java programs. Personal and Ubiquitous Computing, 2(2):109–116, jun 1998.
- [63] Carol J Fung, Olga Baysal, Jie Zhang, Issam Aib, and Raouf Boutaba. Trust management for host-based collaborative intrusion detection. In International Workshop on Distributed Systems: Operations and Management, pages 109–122. Springer, 2008.
- [64] Carol J Fung and Raouf Boutaba. Design and management of collaborative intrusion detection networks. In 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), pages 955–961. IEEE, 2013.
- [65] Carol J Fung, Jie Zhang, Issam Aib, and Raouf Boutaba. Robust and scalable trust management for collaborative intrusion detection. In 2009 IFIP/IEEE International Symposium on Integrated Network Management, pages 33–40. IEEE, 2009.
- [66] Carol J Fung, Quanyan Zhu, Raouf Boutaba, and Tamer Başar. Bayesian decision aggregation in collaborative intrusion detection networks. In 2010 IEEE Network Operations and Management Symposium-NOMS 2010, pages 349–356. IEEE, 2010.
- [67] Sunanda Gamage and Jagath Samarabandu. Deep learning methods in network intrusion detection: A survey and an objective comparison. Journal of Network and Computer Applications, page 102767, 2020.
- [68] Ni Gao, Ling Gao, Quanli Gao, and Hai Wang. An Intrusion Detection Model Based on Deep Belief Networks. In 2014 Second International Conference on Advanced Cloud and Big Data, pages 247–252. IEEE, nov 2014.
- [69] Erik Gawehn, Jan A. Hiss, and Gisbert Schneider. Deep Learning in Drug Discovery. Molecular Informatics, 35(1):3–14, 2016.
- [70] Michael R Genesereth and Nils J Nilsson. Logical foundations of artificial. Intelligence. Morgan Kaufmann, 2, 1987.
- [71] Anup K Ghosh, James Wanken, and Frank Charron. Detecting anomalous and unknown intrusions against programs. In Proceedings 14th annual computer security applications conference (Cat. No. 98Ex217), pages 259–267. IEEE, 1998.
- [72] Rajeev Gopalakrishna and E.H. Spafford. A framework for distributed intrusion detection using interest driven cooperating agents. Proceedings of the 4th International Symposium on Recent Advances in Intrusion Detection (RAID 2001), pages 1–23, 2001.
- [73] Shaw Green, L. Hurst, B. Nangle, and P. Cunningham. Software agents: A review. Technical Report, 66(May):26–39, 1997.
- [74] Sander Greenland, Judea Pearl, James M Robins, and Others. Causal diagrams for epidemiologic research. Epidemiology, 10:37–48, 1999.
- [75] Thomas R. Gruber. A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2):199–220, jun 1993.
- [76] Thomas R. Gruber. Toward principles for the design of ontologies used for knowledge sharing? International Journal of Human-Computer Studies, 43(5-6):907–928, nov 1995.
- [77] Jie Gu, Lihong Wang, Huiwen Wang, and Shanshan Wang. A novel approach to intrusion detection using svm ensemble with feature augmentation. Computers & Security, 86:53–62, 2019.
- [78] Yunchuan Guo, Han Zhang, Lingcui Zhang, Liang Fang, and Fenghua Li. A game theoretic approach to cooperative intrusion detection. Journal of computational science, 30:118–126, 2019.
- [79] Megha Gupta. Hybrid Intrusion Detection System: Technology and Development. International Journal of Computer Applications, 115(9):5–8, apr 2015.
- [80] D. Hammerstrom. Working with neural networks. IEEE Spectrum, 30(7):46–53, jul 1993.
- [81] Khadijah M Hanga and Yevgeniya Kovalchuk. Machine learning and multi-agent systems in oil and gas industry applications: A survey. Computer Science Review, 34:100191, 2019.
- [82] David Heckerman. A tutorial on learning with bayesian networks. Microsoft Research. 1995.
- [83] Álvaro Herrero and Emilio Corchado. Multiagent systems for network intrusion detection: A review. In Computational Intelligence in Security for Information Systems, pages 143–154. Springer, 2009.
- [84] Geoffrey E. Hinton, Simon Osindero, and Yee-Whye Teh. A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, 18(7):1527–1554, jul 2006.
- [85] Neminath Hubballi and Nikhil Tripathi. An event based technique for detecting spoofed ip packets. Journal of Information Security and Applications, 35:32–43, 2017.
- [86] Ezzureen Faznien Ibrahim and Shahrinaz Ismail. Detection ddos using ids in cloud computing. Journal of Computing Technologies and Creative Content (JTec), 3(1):4–6, 2019.
- [87] Mohamed Idhammad, Karim Afdel, and Mustapha Belouch. Distributed intrusion detection system for cloud environments based on data mining techniques. Procedia Computer Science, 127:35–41, 2018.
- [88] James P. Ignizio. A brief introduction to expert systems. Computers & Operations Research, 17(6):523–533, jan 1990.
- [89] Neil C Ingram, Dennis J; Kremer, H S; Rowe. Distributed Intrusion Detection for Computer Systems Using Communicating Agents. MARINE CORPS WARFIGHTING LAB QUANTICO VA, 2000.
- [90] Kuldeep Jachak and Ashish Barua. Distributed intrusion detection using mobile agent in distributed system. IJCA Proceedings on Emerging Trends in Computer Science and Information Technology (ETCSIT2012), 3:1–6, 2012.
- [91] S Janakiraman. An Intelligent Distributed Intrusion Detection System using Genetic Algorithm. Journal of Convergence Information Technology, 4(1):70–76, 2009.
- [92] Wayne Jansen, Peter Mell, Tom Karygiannis, and Don Marks. Applying Mobile Agents to Intrusion Detection and Response. NIST Interim Report (IR) - 6416, (October):1–46, 1999.
- [93] Nicholas R Jennings and Michael Wooldridge. Applications of intelligent agents. In Agent technology, pages 3–28. Springer, 1998.
- [94] Dongzi Jin, Yiqin Lu, Jiancheng Qin, Zhe Cheng, and Zhongshu Mao. Swiftids: Real-time intrusion detection system based on lightgbm and parallel intrusion detection mechanism. Computers & Security, 97:101984, 2020.
- [95] Ak Jones and Rs Sielken. Computer system intrusion detection: A survey. Computer Science Technical Report, pages 1–25, 2000.
- [96] Youna Jung, Minsoo Kim, Amirreza Masoumzadeh, and James BD Joshi. A survey of security issue in multi-agent systems. Artificial Intelligence Review, 37(3):239–260, 2012.
- [97] C Kalimuthan and J Arokia Renjit. Review on intrusion detection using feature selection with machine learning techniques. Materials Today: Proceedings, 2020.
- [98] Pradeep Kannadiga and Mohammad Zulkernine. DIDMA: A distributed intrusion detection system using mobile agents. In Proceedings - Sixth Int. Conf. on Softw. Eng., Artificial Intelligence, Netw. and Parallel/Distributed Computing and First ACIS Int. Workshop on Self-Assembling Wireless Netw., SNPD/SAWN 2005, volume 2005, pages 238–245. IEEE, 2005.
- [99] Shafiullah Khan, Kok Keong Loo, and Zia Ud Din. Framework for intrusion detection in IEEE 802.11 wireless mesh networks. International Arab Journal of Information Technology, 7(4):435–440, 2010.
- [100] Supriya Khobragade and Puja Padiya. Distributed Intrusion Detection System Using Mobile Agent. International Journal of Engineering and Innovative Technology (IJEIT), 5(4), 2015.
- [101] Serkan Kiranyaz. Particle swarm optimization. In Adaptation, Learning, and Optimization, volume 15, pages 45–82. Citeseer, 2014.
- [102] Sotiris B Kotsiantis, I Zaharakis, and P Pintelas. Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160:3–24, 2007.
- [103] Praful Koturwar, Sheetal Girase, and Debajyoti Mukhopadhyay. A Survey of Classification Techniques in the Area of Big Data. mar 2015.
- [104] Tiina Kovanen, Gil David, and Timo Hämäläinen. Survey: Intrusion detection systems in encrypted traffic. In Internet of Things, Smart Spaces, and Next Generation Networks and Systems, pages 281–293. Springer, 2016.
- [105] C. Kruegel, D. Mutz, W. Robertson, and F. Valeur. Bayesian event classification for intrusion detection. In 19th Annual Computer Security Applications Conference, 2003. Proceedings., pages 14–23. IEEE, 1999.
- [106] Christopher Kruegel and Thomas Toth. Distributed Pattern Detection for Instrusion Detection. Ndss, 1, 2002.
- [107] Tsuang Kuo, Anil Mital, and Sam Anand. An introduction to expert systems in production and manufacturing engineering: the structure, development process and applications. In Handbook of Expert Systems Applications in Manufacturing Structures and rules, pages 1–20. Springer Netherlands, Dordrecht, 1994.
- [108] Donghwoon Kwon, Hyunjoo Kim, Jinoh Kim, Sang C. Suh, Ikkyun Kim, and Kuinam J. Kim. A survey of deep learning-based network anomaly detection. Cluster Computing, 22(S1):949–961, jan 2019.
- [109] Safaa Laqtib, Khalid El Yassini, and Moulay Lahcen Hasnaoui. A deep learning methods for intrusion detection systems based machine learning in manet. In Proceedings of the 4th International Conference on Smart City Applications, pages 1–8, 2019.
- [110] Alina Lazar. Heuristic Knowledge Discovery for Archaeological Data Using Genetic Algorithms and Rough Sets. Heuristic and Optimization for Knowledge Discovery, pages 263–278, 2011.
- [111] S.C. Lee and D.V. Heinbuch. Training a neural-network based intrusion detector to recognize novel attacks. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 31(4):294–299, jul 2001.
- [112] Wenke Lee and Salvatore J. Stolfo. A framework for constructing features and models for intrusion detection systems. ACM Transactions on Information and System Security, 3(4):227–261, nov 2000.
- [113] Wei Li. Using genetic algorithm for network intrusion detection. Proceedings of the United States Department of Energy Cyber Security Group 2004 Training Conference, Kansas City, Kansas, 1:24–27, 2004.
- [114] Wenjuan Li and Lam For Kwok. Challenge-based collaborative intrusion detection networks under passive message fingerprint attack: a further analysis. Journal of Information Security and Applications, 47:1–7, 2019.
- [115] Yongzhong Li, Miao Du, and Jing Xu. A New Distributed Intrusion Detection Method Based on Immune Mobile Agent. In Proceedings - 2018 6th International Conference on Advanced Cloud and Big Data, CBD 2018, pages 215–219. IEEE, 2018.
- [116] Hung-Jen Liao, Chun-Hung Richard Lin, Ying-Chih Lin, and Kuang-Yuan Tung. Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications, 36(1):16–24, jan 2013.
- [117] Martin Andreoni Lopez, Diogo Menezes Ferrazani Mattos, and Otto Carlos M. B. Duarte. An elastic intrusion detection system for software networks. Annals of Telecommunications, 71(11–12):595–605, dec 2016.
- [118] Manuel Lopez-Martin, Belen Carro, and Antonio Sanchez-Esguevillas. Application of deep reinforcement learning to intrusion detection for supervised problems. Expert Systems with Applications, 141:112963, 2020.
- [119] Gehao Lu and Joan Lu. Background review for neural trust and multi-agent system. In Natural Language Processing: Concepts, Methodologies, Tools, and Applications, pages 1–22. IGI Global, 2020.
- [120] Namratha M and Prajwala TR. A Comprehensive Overview of Clustering Algorithms in Pattern Recognition. IOSR Journal of Computer Engineering, 4(6):23–30, 2012.
- [121] Jamila Manan, Atiq Ahmed, Ihsan Ullah, Leïla Merghem-Boulahia, and Dominique Gaïti. Distributed intrusion detection scheme for next generation networks. Journal of Network and Computer Applications, 147:102422, 2019.
- [122] Frank Manola, Eric Miller, Brian McBride, and Others. RDF primer. W3C recommendation, 10(1–107):6, 2004.
- [123] Mirco Marchetti, Fabio Pierazzi, Michele Colajanni, and Alessandro Guido. Analysis of high volumes of network traffic for advanced persistent threat detection. Computer Networks, 109:127–141, 2016.
- [124] Adam. MARCZYK. Genetic algorithms and evolutionary programing. Studies in Computational Intelligence, 652:309–348, 2017.
- [125] Naila Marir, Huiqiang Wang, Guangsheng Feng, Bingyang Li, and Meijuan Jia. Distributed Abnormal Behavior Detection Approach Based on Deep Belief Network and Ensemble SVM Using Spark. IEEE Access, 6:59657–59671, 2018.
- [126] Guozhu Meng, Yang Liu, Jie Zhang, Alexander Pokluda, and Raouf Boutaba. Collaborative security: A survey and taxonomy. ACM Computing Surveys (CSUR), 48(1):1–42, 2015.
- [127] Negnevitsky Michael. Artificial intelligence a guide to intelligent systems, 2005.
- [128] H Sardana Milan and Kamalpreet Singh. Reducing false alarms in intrusion detection systems–a survey. International Research Journal of Engineering and Technology (IRJET) e-ISSN, pages 2395–0056, 2018.
- [129] Webb Miller and Eugene W. Myers. A file comparison program. Software: Practice and Experience, 15(11):1025–1040, nov 1985.
- [130] Jelena Mirkovic, Gregory Prier, and Peter Reiher. Attacking ddos at the source. In 10th IEEE International Conference on Network Protocols, 2002. Proceedings., pages 312–321. IEEE, 2002.
- [131] Chirag Modi, Dhiren Patel, Bhavesh Borisaniya, Hiren Patel, Avi Patel, and Muttukrishnan Rajarajan. A survey of intrusion detection techniques in Cloud. Journal of Network and Computer Applications, 36(1):42–57, jan 2013.
- [132] Eugene W. Myers. AnO(ND) difference algorithm and its variations. Algorithmica, 1(1-4):251–266, nov 1986.
- [133] Abdenacer Nafir, Smaine Mazouzi, and Salim Chikhi. Collective intrusion detection in wide area networks. INISTA 2014 - IEEE International Symposium on Innovations in Intelligent Systems and Applications, Proceedings, pages 46–51, 2014.
- [134] Maria Nenova, Denis Atanasov, Kiril Kassev, and Andon Nenov. Intrusion detection system model implementation against ddos attacks. In 2019 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems (COMCAS), pages 1–4. IEEE, 2019.
- [135] Minh Tuan Nguyen and Kiseon Kim. Genetic convolutional neural network for intrusion detection systems. Future Generation Computer Systems, 113:418–427, 2020.
- [136] O Oriola, AB Adeyemo, and ABC Robert. Distributed intrusion detection system using p2p agent mining scheme. African Journal of Computing & ICT, 5(2):3–10, 2012.
- [137] Suad Mohammed Othman, Nabeel T Alsohybe, Fadl Mutaher Ba-Alwi, and Ammar Thabit Zahary. Survey on intrusion detection system types. International Journal of Cyber-Security and Digital Forensics, 7(4):444–463, 2018.
- [138] Amrit Pal Singh and Manik Deep Singh. Analysis of Host-Based and Network-Based Intrusion Detection System. International Journal of Computer Network and Information Security, 6(8):41–47, jul 2014.
- [139] Nicholas Pappas. Network IDS and IPS Deployment Strategies. SANS Institute, 2008.
- [140] Animesh Patcha and Jung Min Park. An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks, 51(12):3448–3470, aug 2007.
- [141] Marek Pawlicki, Michał Choraś, and Rafał Kozik. Defending network intrusion detection systems against adversarial evasion attacks. Future Generation Computer Systems, 2020.
- [142] Sandhya Peddabachigari, Ajith Abraham, and Johnson Thomas. Intrusion Detection Systems Using Decision Trees and Support Vector Machines. International Journal of Applied Science and Computations, 11(3):118–134, 2004.
- [143] Daniel Pérez, Serafín Alonso, Antonio Morán, Miguel A. Prada, Juan José Fuertes, and Manuel Domínguez. Comparison of Network Intrusion Detection Performance Using Feature Representation. pages 463–475. 2019.
- [144] Stavros Petridis, Themos Stafylakis, Pingehuan Ma, Feipeng Cai, Georgios Tzimiropoulos, and Maja Pantic. End-to-End Audiovisual Speech Recognition. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6548–6552. IEEE, apr 2018.
- [145] John Pinkston, Jeffrey Undercoffer, Anupam Joshi, and Timothy Finin. A target-centric ontology for intrusion detection. In In proceeding of the IJCAI-03 Workshop on Ontologies and Distributed Systems. Acapulco, August 9 th. Citeseer, 2004.
- [146] Hartmnt Pohlheim. “Genetic and Evolutionary Algorithms: Principles, Methods and Algorithms.” Genetic and Evolutionary Algorithm Tool-box. Evolutionäre Algorithmen, 30, 2001.
- [147] J Ross Quinlan. Constructing decision tree. C4, 5:17–26, 1993.
- [148] Shahid Raza, Linus Wallgren, and Thiemo Voigt. SVELTE: Real-time intrusion detection in the Internet of Things. Ad Hoc Networks, 11(8):2661–2674, nov 2013.
- [149] R. Ravinder Reddy, Y Ramadevi, and K. V. N Sunitha. Effective discriminant function for intrusion detection using SVM. In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pages 1148–1153. IEEE, sep 2016.
- [150] Ren Hui Gong, M. Zulkernine, and P. Abolmaesumi. A Software Implementation of a Genetic Algorithm Based Approach to Network Intrusion Detection. In Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks (SNPD/SAWN’05), pages 246–253. IEEE.
- [151] Hamed Rezaee and Farzaneh Abdollahi. Average consensus over high-order multiagent systems. IEEE Transactions on Automatic Control, 60(11):3047–3052, 2015.
- [152] AHM Rezaul Karim, RMAP Rajatheva, and Kazi M Ahmed. An efficient collaborative intrusion detection system for manet using bayesian approach. In Proceedings of the 9th ACM international symposium on Modeling analysis and simulation of wireless and mobile systems, pages 187–190, 2006.
- [153] Markus Ring, Sarah Wunderlich, Deniz Scheuring, Dieter Landes, and Andreas Hotho. A survey of network-based intrusion detection data sets. Computers & Security, 86:147–167, 2019.
- [154] 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, 9(6):4951–4960, 2019.
- [155] Dorothy Elizabeth Robling Denning. Cryptography and data security. Addison-Wesley Longman Publishing Co., Inc., 1982.
- [156] Rodrigo Roman, Jianying Zhou, and Javier Lopez. On the features and challenges of security and privacy in distributed internet of things. Computer Networks, 57(10):2266–2279, 2013.
- [157] Antony Rowstron and Peter Druschel. Pastry: Scalable, decentralized object location, and routing for large-scale peer-to-peer systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 2218, pages 329–350. 2001.
- [158] S J Russell and P Norvig. Artificial Intelligence: A Modern Approach Prentice Hall. New Jersey, 1995.
- [159] Jake Ryan, Meng-Jang Lin, and Risto Miikkulainen. Intrusion detection with neural networks. In Advances in neural information processing systems, pages 943–949, 1998.
- [160] Jean-Marc Seigneur, Adam Slagell, Jean-Marc Seigneur, and Adam Slagell. Collaborative Computer Security and Trust Management. Information Science Reference, 2010.
- [161] D Selvamani and V Selvi. An efficacious intellectual framework for host based intrusion detection system. Procedia Computer Science, 165:9–17, 2019.
- [162] Jaydip Sen. A Distributed Intrusion Detection System Using Cooperating Agents. arXiv preprint, nov 2011.
- [163] Shahaboddin Shamshirband, Samira Kalantari, Z Sam Daliri, and Liang Shing Ng. Expert security system in wireless sensor networks based on fuzzy discussion multi-agent systems. Scientific Research and Essays, 5(24):3840–3849, 2010.
- [164] Nathan Shone, Tran Nguyen Ngoc, Vu Dinh Phai, and Qi Shi. A Deep Learning Approach to Network Intrusion Detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1):41–50, 2018.
- [165] Ahmed F. Shosha, Pavel Gladyshev, Shinn-Shyan Wu, and Chen-Ching Liu. Detecting cyber intrusions in SCADA networks using multi-agent collaboration. In 2011 16th International Conference on Intelligent System Applications to Power Systems, pages 1–7. IEEE, sep 2011.
- [166] Zhai Shuang-Can, Hu Chen-jun, and Zhang Weiming. Multi-agent distributed intrusion detection system model based on BP neural network. International Journal of Security and its Applications, 8(2):183–192, 2014.
- [167] Abhishek Singh, Ola Nordström, Chenghuai Lu, and Andre LM Dos Santos. Malicious icmp tunneling: Defense against the vulnerability. In Australasian Conference on Information Security and Privacy, pages 226–236. Springer, 2003.
- [168] Ankush Singla and Elisa Bertino. How Deep Learning Is Making Information Security More Intelligent. IEEE Security and Privacy, 17(3):56–65, 2019.
- [169] Steven R. Snapp, James Brentano, Gihan V. Dias, Terrance L. Goan, L. Todd Heberlein, Che-Lin Ho, Karl N. Levitt, Biswanath Mukherjee, Stephen E. Smaha, Tim Grance, Daniel M. Teal, and Doug Mansur. DIDS (Distributed intrusion detection system) - Motivation, architecture, and an early prototype. Proceedings of the 14th national computer security conference, pages 1–9, 1991.
- [170] Krzysztof Socha and Marco Dorigo. Ant colony optimization for continuous domains. European Journal of Operational Research, 185(3):1155–1173, mar 2008.
- [171] Eugene H Spafford and Diego Zamboni. Intrusion detection using autonomous agents. Computer Networks, 34(4):547–570, oct 2000.
- [172] Gary Stein, Bing Chen, Annie S. Wu, and Kien A. Hua. Decision tree classifier for network intrusion detection with GA-based feature selection. In Proceedings of the 43rd annual southeast regional conference on - ACM-SE 43, volume 2, page 136, New York, New York, USA, 2005. ACM Press.
- [173] Nasrin Sultana, Naveen Chilamkurti, Wei Peng, and Rabei Alhadad. Survey on SDN based network intrusion detection system using machine learning approaches. Peer-to-Peer Networking and Applications, 12(2):493–501, mar 2019.
- [174] Sung-Bae Cho. Incorporating soft computing techniques into a probabilistic intrusion detection system. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 32(2):154–160, may 2002.
- [175] K. S. Tang, K. F. Man, S. Kwong, and Q. He. Genetic algorithms and their applications. IEEE Signal Processing Magazine, 13(6):22–37, 1996.
- [176] Tuan A. Tang, Lotfi Mhamdi, Des McLernon, Syed Ali Raza Zaidi, and Mounir Ghogho. Deep learning approach for Network Intrusion Detection in Software Defined Networking. Proceedings - 2016 International Conference on Wireless Networks and Mobile Communications, WINCOM 2016: Green Communications and Networking, pages 258–263, 2016.
- [177] Shaohua Teng, Naiqi Wu, Haibin Zhu, Luyao Teng, and Wei Zhang. Svm-dt-based adaptive and collaborative intrusion detection. IEEE/CAA Journal of Automatica Sinica, 5(1):108–118, 2017.
- [178] Abebe Tesfahun and D. Lalitha Bhaskari. Effective Hybrid Intrusion Detection System: A Layered Approach. International Journal of Computer Network and Information Security, 7(3):35–41, feb 2015.
- [179] Rajendra Tiwari and R Gour. Mobile agent based distributed intrusion detection system: A survey. International Journal of Computer Applications in Engineering Sciences, 2, 2012.
- [180] Trushna Tushar Khose Patil; and C.O. Banchho. A survey on Mobile Agent Based Intrusion Detection System. International Journal of Advanced Research in Computer and Communication Engineering, 1:773–777, 2012.
- [181] E. Turban and J.E. Aronson. Expert Systems and Intelligent Systems. Prentice Hall, page 865, 2001.
- [182] Esko Ukkonen. Algorithms for approximate string matching. Information and Control, 64(1–3):100–118, jan 1985.
- [183] Emmanouil Vasilomanolakis, Shankar Karuppayah, Max Mühlhäuser, and Mathias Fischer. Taxonomy and survey of collaborative intrusion detection. ACM Computing Surveys (CSUR), 47(4):1–33, 2015.
- [184] J. J. Verbeek, N. Vlassis, and B. Kröse. Efficient Greedy Learning of Gaussian Mixture Models. Neural Computation, 15(2):469–485, feb 2003.
- [185] Theuns Verwoerd and Ray Hunt. Intrusion detection techniques and approaches. Computer communications, 25(15):1356–1365, 2002.
- [186] Richard A VIGNA, Giovanni et KEMMERER. NetSTAT: A network-based intrusion detection system. Journal of computer security, 7(1):37–71, 1999.
- [187] Stefan Voß, Silvano Martello, Ibrahim H Osman, and Catherine Roucairol. Meta-heuristics: Advances and trends in local search paradigms for optimization. Springer Science & Business Media, 2012.
- [188] Ajinkya Wankhade and K. Chandrasekaran. Distributed-Intrusion Detection System using combination of Ant Colony Optimization (ACO) and support vector machine (SVM). Proceedings - 2016 International Conference on Micro-Electronics and Telecommunication Engineering, ICMETE 2016, pages 646–651, 2016.
- [189] Hervé; WESPI, Andreas; DACIER, Marc; DEBAR. Intrusion detection using variable-length audit trail patterns. In: International Workshop on Recent Advances in Intrusion Detection. Springer, Berlin, Heidelberg, 1907:110–129, 2000.
- [190] Danny Weyns, Elke Steegmans, and Tom Holvoet. Protocol-based communication for situated multi-agent systems. Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2004, 1:118–125, 2004.
- [191] Benjamin Wilken and Massimiliano Antonio Poletto. Connection based detection of scanning attacks, May 11 2010. US Patent 7,716,737.
- [192] Michael Wooldridge and Nicholas R Jennings. Intelligent agents: Theory and practice. The knowledge engineering review, 10(2):115–152, 1995.
- [193] Shelly Xiaonan Wu and Wolfgang Banzhaf. The use of computational intelligence in intrusion detection systems: A review. Applied Soft Computing, 10(1):1–35, jan 2010.
- [194] Sun Wu, Udi Manber, Gene Myers, and Webb Miller. An O(NP) sequence comparison algorithm. Information Processing Letters, 35(6):317–323, sep 1990.
- [195] Akira Yamada, Yutaka Miyake, Keisuke Takemori, Ahren Studer, and Adrian Perrig. Intrusion detection for encrypted web accesses. In 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW’07), volume 1, pages 569–576. IEEE, 2007.
- [196] Jianhua Yang and Shou-Hsuan Stephen Huang. Matching TCP/IP packets to detect stepping-stone intrusion. International Journal of Computer Science and Network Security, 6(4):269–276, 2006.
- [197] Liu Hua Yeo, Xiangdong Che, and Shalini Lakkaraju. Understanding Modern Intrusion Detection Systems: A Survey. arXiv preprint arXiv:1708.07174, 2017.
- [198] Jaehak Yu, Hansung Lee, Myung-Sup Kim, and Daihee Park. Traffic flooding attack detection with snmp mib using svmq. Computer Communications, 31:4212–4219, 2008.
- [199] Yuening Zhang, Yiming Zhang, Nan Zhang, and Mingzhong Xiao. A network intrusion detection method based on deep learning with higher accuracy. Procedia Computer Science, 174:50–54, 2020.
- [200] Zheng Zhang, Scott Schwartz, Lukas Wagner, and Webb Miller. A Greedy Algorithm for Aligning DNA Sequences. Journal of Computational Biology, 7(1-2):203–214, feb 2000.
- [201] Chenfeng Vincent Zhou, Christopher Leckie, and Shanika Karunasekera. A survey of coordinated attacks and collaborative intrusion detection. Computers & Security, 29(1):124–140, 2010.
- [202] Man Zhou, Lansheng Han, Hongwei Lu, and Cai Fu. Distributed collaborative intrusion detection system for vehicular ad hoc networks based on invariant. Computer Networks, page 107174, 2020.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-98ca5804-bfad-482b-b295-846901b195c1