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.

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.

[] 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.

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.

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