GNIDS: rule-based network intrusion detection system using genetic algorithms

Abstract

Detection of intrusions in computer networks has been a growing problem motivating widespread research in computer science to develop better Intrusion Detecting Systems IDS. The existing IDS have been quite static and lack the ability to adjust themselves to the new network traffic and hence new kinds of attack. In this paper, we present a genetic algorithm GA based machine learning approach to identify such harmful/attack type of connections. The algorithm takes into consideration different features in network connections such as source and destination IP, type of protocol and status of the connection to generate a classification rule set. The proposed method is efficient with respect to good detection rate and low false positives. The experimental results demonstrate the lower execution time of the proposed algorithm. The 1999 DARPA IDS dataset is used as the evaluation dataset for both training and testing.