ANTIDS: Self Orga nized Ant-Based C lustering Model for Intrusion Det ection System

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, integri ty, and availability. Due to the fact that it is almost difficult for a system administrator to recognize and manually intervene to stop an attack, there is an increasing re cognition that Int rusion Detection Systems (IDS) sh ould have a lot to earn on following i ts basic principle s 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. Having that aim in mind, the present work p resents a self-organized ANT colony based Intrusion Detection System (ANTIDS) to detect intrusions in a network infrastru cture. The performance is compared among convention al soft computing paradigms like Dec ision Trees (DT), Support Vector Machines (SVM) and Linear Genetic Programming (LGP) to model fast, online and efficient intrusion detection systems.

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