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A FEATURE SELECTION AGENT-BASED IDS

This paper introduces an Intrusion Detection System (IDS) based on the use of several Artificial Intelligence (AI) techniques. The anomalous detection issue is approached from a feature selection point of view, where a connectionist model is applied as a data analysis technique in an IDS. By exploiting the strengths of connectionist architectures in recognition, classification and generalization, this work shows the benefits of applying connectionist models and agent technology to the Intrusion Detection (ID) field. This work is based on the fact that projectionist systems have never been applied to the IDS field and network security until this research project. It helps network administrators to decide if anomalous situations are real intrusions or not. To cover the more complex situations related to segment-divided networks, we introduce a new approach based on a distributed architecture where different software agents cooperate to detect anomalous SNMP (Simple Network Management Protocol) situations in a big-size network.