Combining Genetic-Based Misuse and Anomaly Detection for Reliably Detecting Intrusions in Computer Networks

When addressing the problem of detecting malicious activities within network traffic, one of the main concerns is the reliability of the packet classification. Furthermore, a system able to detect the so-called

zero-day attacks

is desirable. Pattern recognition techniques have proven their generalization ability in detecting intrusions, and systems based on multiple classifiers can enforce the detection reliability by combining and correlating the results obtained by different classifiers.

In this paper we present a system exploiting genetic algorithms for deploying both a misuse-based and an anomaly-based classifier. Hence, by suitably combining the results obtained by means of such techniques, we aim at attaining a highly reliable classification system, still with a significant degree of new attack prediction ability. In order to improve classification reliability, we introduce the concept of rejection: instead of emitting an unreliable verdict, an ambiguous packet can be logged for further analysis. Tests of the proposed system on a standard database for benchmarking intrusion detection systems are also reported.