Traffic Data Preparation for a Hybrid Network IDS

An increasing effort has being devoted to researching on the field of Intrusion Detection Systems (IDS's). A wide variety of artificial intelligence techniques and paradigms have been applied to this challenging task in order to identify anomalous situations taking place within a computer network. Among these techniques is the neural network approach whose models (or most of them) have some difficulties in processing traffic data "on the fly". The present work addresses this weakness, emphasizing the importance of an appropriate segmentation of raw traffic data for a successful network intrusion detection relying on unsupervised neural models. In this paper, the presented neural model is embedded in a hybrid artificial intelligence IDS which integrates the case based reasoning and multiagent paradigms.