Evaluation of an adaptive genetic-based signature extraction system for network intrusion detection

Machine learning techniques are frequently applied to intrusion detection problems in various ways such as to classify normal and intrusive activities or to mine interesting intrusion patterns. Self-learning rule-based systems can relieve domain experts from the difficult task of hand crafting signatures, in addition to providing intrusion classification capabilities. To this end, a genetic-based signature learning system has been developed that can adaptively and dynamically learn signatures of both normal and intrusive activities from the network traffic. In this paper, we extend the evaluation of our systems to real time network traffic which is captured from a university departmental server. A methodology is developed to build fully labelled intrusion detection data set by mixing real background traffic with attacks simulated in a controlled environment. Tools are developed to pre-process the raw network data into feature vector format suitable for a supervised learning classifier system and other related machine learning systems. The signature extraction system is then applied to this data set and the results are discussed. We show that even simple feature sets can help detecting payload-based attacks.