Dynamic network traffic data classification for intrusion detection using genetic algorithm

Abstract

Intrusion Detection System (IDS) classifies network traffic data either 'anomaly' or 'normal' to protect computer systems from different types of attacks. In this paper, data mining concepts and genetic algorithm have been applied to classify online traffic data efficiently by developing a rule based lazy classifier. The proposed method updates the rule set dynamically to accommodate the changing pattern in the traffic data in order to attain highest classification accuracy and at the same time maintaining consistency. The classifier is able to detect variants of common network traffic data patterns or modified existing security attacks based on the knowledge gained from its existing training data set with significant classification accuracy. © 2012 Springer-Verlag.