Self-Organized Ant-based Clustering Model for Intrusion
Detection Systems (ANTIDS)
54. Vitorino Ramos, Ajith
Abraham, ANTIDS: Self-Organized Ant-based Clustering Model for
Intrusion Detection System, in Swarm Intelligence and Patterns
special session at WSTST-05 - 4th IEEE Int. Conf. on Soft Computing as
Transdisciplinary Science and Technology - Japan, LNCS series, Springer-Verlag, Germany,
pp. 977-986, May 2005.
Figure - Self-Organized Ant-based clustering results on IDS data (MIT Lincoln Labs) using a full data set with 11982 samples (41 features each) in the initial and final steps.
PDF file: paper (389 Kb)
Abstract: Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: the external intruders who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. Due to the fact that it is more and more improbable to a system administrator to recognize and manually intervene to stop an attack, there is an increasing recognition that ID systems should have a lot to earn on following its basic principles on the behavior of complex natural systems, namely in what refers to self-organization, allowing for a real distributed and collective perception of this phenomena. With that aim in mind, the present work presents a self-organized ant colony based intrusion detection system (ANTIDS) to detect intrusions in a network infrastructure. The performance is compared among conventional soft computing paradigms like Decision Trees, Support Vector Machines and Linear Genetic Programming to model fast, online and efficient intrusion detection systems.
º Bin Zhang, Yi-Dan Su, "An Ant Colony Clustering Algorithm Based on Directional Similarity: ACCADS", in Computer and Modernization Journal, n. 3, pp. 86-89, China, 2008.
º Kamran Shafi, Hussein A. Abbass, "Biologically-inspired Complex Adaptive Systems approaches to Network Intrusion Detection", in Information Security Journal, Vol. 12, Issue 4, pp. 209-217, Elsevier, Jan. 2007.
º O. Yadgar, "From Local Search To Global Behavior: Ad Hoc Network Example", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4676 LNAI, pp. 186-208, Springer, 2007.
º Julia Handl and Bernd Meyer, "Ant-based and Swarm-based clustering", in Swarm Intelligence Journal, 1(2):95-113, Springer, 2007.
º Osher Yadgar, "Cooperative Consensus Formation in Large-Scale MAS under the N-Person Prisoner’s Dilemma", AIC Lab. Menlo Park, CA, USA, 2007.
º Bart Gilner, "A Comparative Study Of Ant Clustering Algorithms", in Msc Thesis, University of Maastricht, Department of Mathematics, Netherlands, October 2007.
º Haoxiang Xia, Shuguang Wang and Taketoshi Yoshida, "A Modified Ant-based Text Clustering Algorithm with Semantic Similarity measure", in Journal of Systems Science and Systems Engineering, Springer-Verlag GmbH, ISSN 1004-3756, Vol. 15, Number 4, pp. 474-492 December, 2006.
º Gülüzar Kekec, Nejat Yumusak, Numan Celebi, "Data Mining and Clustering with Ant Colony Optimization", in Proceedings of 5th International Symposium on Intelligent Manufacturing Systems, May 29-31, pp. 1178-1190, 2006.
º Xu Xiao-Hua, Chen Ling, "An Adaptive Ant Clustering Algorithm", in Journal of Software, pp. 1884-1889, Sept. 2006.
º Srinoy, S., Chimphlee, W., Chimphlee, S., Poopaibool, Y., "An Approach to solve Computer Attacks based on Hybrid model", in WSEAS Transactions on Computers, Vol. 5, Issue 6, pp. 1280-1284, June 2006.
º Chi-Ho Tsang, Sam Kwong, "Ant Colony Clustering and Feature Extraction for Anomaly Intrusion Detection", in Swarm Intelligence in Data Mining, A. Abraham, C. Grosan, V. Ramos (Eds.), Studies in Computational Intelligence (series), Vol. 34, pp. 102-123, Springer, Germany, Set. 2006.
º Srinoy, S., Kurutach, W., Chimphlee, W., Chimphlee, S., Sounsri, S., "Intrusion Detection via Independent Component Analysis based on Rough Fuzzy", in WSEAS Transactions on Computers, Vol.5, Issue 1, pp. 43-48, January 2006.
º Ajith Abraham, Crina Grosan, Carlos Martin-Vide, "Evolutionary Design of Intrusion Detection Programs", in International Journal of Network Security, 2006.
º Yun Wang, Inyoung Kim, Gaston Mbateng, Shih-Yieh Ho, "A Latent Class Modeling approach to Detect Network Intrusion", in Computer Communications Journal, Vol. 30, Issue 1, pp. 93-100, 2006.
º Chunlai Zhou, Zhigang Li, "The approach of concept designing of the products based on Ant Clustering", in 05 IEEE International Conference on Information Acquisition, ISBN: 0-7803-9303-1, June-July 2005.
º Jason Shifflet, "A Technique Independent Fusion Model for Network Intrusion Detection", in MCURCSM´05 - Proc. of the Midstates Conf. on Undergraduate Research in Computer Science and Mathematics, Vol. 3, Nº 1, pp. 13-19, USA, Oct. 2005.
º Maynard Exum,"Self-Organized Data Clustering with the help of Swarm Agents", Database Systems CS541 Class presentation (Instructor: Dr. Amin A. Abdulghani), Rutgers University, Departm. of Computer Science, New Jersey, USA, 2005.
º Soumya Banerjee, Crina Grosan, Ajith Abraham, "IDEAS: Intrusion Detection based on Emotional Ants for Sensors", in ISDA-05, 5th Int. Conf. on Intelligent Systems, Design and Applications, Wroclaw, Poland, 8-10 September 2005.
º Ajith Abraham, Crina Grosan, Yuehui Chen,"Cyber Security and the Evolution of Intrusion Detection Systems", in Journal of Educational Technology, Special Issue in Knowledge Management, ISSN 0973-0559, I-Manager Publications, India, 2005.
45. Swarms on Continuous Data.
48. Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming.
52. Intrusion Detection Systems using Adaptive Regression Splines.
63. Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes.
39. Self-Organized Data and Image Retrieval as a Consequence of Inter-Dynamic Synergistic Relationships in Artificial Ant Colonies.
42. Self-Organized Stigmergic Document Maps: Environment as a Mechanism for Context Learning.
29. Artificial Ant Colonies in Digital Image Habitats - A Mass Behaviour Effect Study on Pattern Recognition.
Figure - Self-Organized Ant-based clustering results on IDS data (MIT Lincoln Labs) using a full data set with 11982 samples (41 features each) in the initial and final steps.
PDF file: paper (389 Kb)
Abstract: Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: the external intruders who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. Due to the fact that it is more and more improbable to a system administrator to recognize and manually intervene to stop an attack, there is an increasing recognition that ID systems should have a lot to earn on following its basic principles on the behavior of complex natural systems, namely in what refers to self-organization, allowing for a real distributed and collective perception of this phenomena. With that aim in mind, the present work presents a self-organized ant colony based intrusion detection system (ANTIDS) to detect intrusions in a network infrastructure. The performance is compared among conventional soft computing paradigms like Decision Trees, Support Vector Machines and Linear Genetic Programming to model fast, online and efficient intrusion detection systems.
Keywords: Network security, Intrusion Detection System, Swarm Intelligence, Bio-Inspired Ant-like Clustering, Soft Computing and Stigmergy.
Cited by:º Bin Zhang, Yi-Dan Su, "An Ant Colony Clustering Algorithm Based on Directional Similarity: ACCADS", in Computer and Modernization Journal, n. 3, pp. 86-89, China, 2008.
º Kamran Shafi, Hussein A. Abbass, "Biologically-inspired Complex Adaptive Systems approaches to Network Intrusion Detection", in Information Security Journal, Vol. 12, Issue 4, pp. 209-217, Elsevier, Jan. 2007.
º O. Yadgar, "From Local Search To Global Behavior: Ad Hoc Network Example", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4676 LNAI, pp. 186-208, Springer, 2007.
º Julia Handl and Bernd Meyer, "Ant-based and Swarm-based clustering", in Swarm Intelligence Journal, 1(2):95-113, Springer, 2007.
º Osher Yadgar, "Cooperative Consensus Formation in Large-Scale MAS under the N-Person Prisoner’s Dilemma", AIC Lab. Menlo Park, CA, USA, 2007.
º Bart Gilner, "A Comparative Study Of Ant Clustering Algorithms", in Msc Thesis, University of Maastricht, Department of Mathematics, Netherlands, October 2007.
º Haoxiang Xia, Shuguang Wang and Taketoshi Yoshida, "A Modified Ant-based Text Clustering Algorithm with Semantic Similarity measure", in Journal of Systems Science and Systems Engineering, Springer-Verlag GmbH, ISSN 1004-3756, Vol. 15, Number 4, pp. 474-492 December, 2006.
º Gülüzar Kekec, Nejat Yumusak, Numan Celebi, "Data Mining and Clustering with Ant Colony Optimization", in Proceedings of 5th International Symposium on Intelligent Manufacturing Systems, May 29-31, pp. 1178-1190, 2006.
º Xu Xiao-Hua, Chen Ling, "An Adaptive Ant Clustering Algorithm", in Journal of Software, pp. 1884-1889, Sept. 2006.
º Srinoy, S., Chimphlee, W., Chimphlee, S., Poopaibool, Y., "An Approach to solve Computer Attacks based on Hybrid model", in WSEAS Transactions on Computers, Vol. 5, Issue 6, pp. 1280-1284, June 2006.
º Chi-Ho Tsang, Sam Kwong, "Ant Colony Clustering and Feature Extraction for Anomaly Intrusion Detection", in Swarm Intelligence in Data Mining, A. Abraham, C. Grosan, V. Ramos (Eds.), Studies in Computational Intelligence (series), Vol. 34, pp. 102-123, Springer, Germany, Set. 2006.
º Srinoy, S., Kurutach, W., Chimphlee, W., Chimphlee, S., Sounsri, S., "Intrusion Detection via Independent Component Analysis based on Rough Fuzzy", in WSEAS Transactions on Computers, Vol.5, Issue 1, pp. 43-48, January 2006.
º Ajith Abraham, Crina Grosan, Carlos Martin-Vide, "Evolutionary Design of Intrusion Detection Programs", in International Journal of Network Security, 2006.
º Yun Wang, Inyoung Kim, Gaston Mbateng, Shih-Yieh Ho, "A Latent Class Modeling approach to Detect Network Intrusion", in Computer Communications Journal, Vol. 30, Issue 1, pp. 93-100, 2006.
º Chunlai Zhou, Zhigang Li, "The approach of concept designing of the products based on Ant Clustering", in 05 IEEE International Conference on Information Acquisition, ISBN: 0-7803-9303-1, June-July 2005.
º Jason Shifflet, "A Technique Independent Fusion Model for Network Intrusion Detection", in MCURCSM´05 - Proc. of the Midstates Conf. on Undergraduate Research in Computer Science and Mathematics, Vol. 3, Nº 1, pp. 13-19, USA, Oct. 2005.
º Maynard Exum,"Self-Organized Data Clustering with the help of Swarm Agents", Database Systems CS541 Class presentation (Instructor: Dr. Amin A. Abdulghani), Rutgers University, Departm. of Computer Science, New Jersey, USA, 2005.
º Soumya Banerjee, Crina Grosan, Ajith Abraham, "IDEAS: Intrusion Detection based on Emotional Ants for Sensors", in ISDA-05, 5th Int. Conf. on Intelligent Systems, Design and Applications, Wroclaw, Poland, 8-10 September 2005.
º Ajith Abraham, Crina Grosan, Yuehui Chen,"Cyber Security and the Evolution of Intrusion Detection Systems", in Journal of Educational Technology, Special Issue in Knowledge Management, ISSN 0973-0559, I-Manager Publications, India, 2005.
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39. Self-Organized Data and Image Retrieval as a Consequence of Inter-Dynamic Synergistic Relationships in Artificial Ant Colonies.
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