Anomaly Intrusion Detection using Multi-Objective Genetic Fuzzy System and Agent-based Evolutionary Computation Framework Chi-Ho Tsang, Sam Kwong, and Hanli Wang   View Paper (PDF)   Return to Session 16: Learning Techniques II Abstract In this paper, we present a multi-objective genetic fuzzy system for anomaly intrusion detection. The proposed system extracts accurate and interpretable fuzzy rule-based knowledge from network data using an agent-based evolutionary computation framework. The experimental results on KDD-Cup99 intrusion detection benchmark data demonstrate that our system can achieve high detection rate for intrusion attacks and low false positive rate for normal network traffic. ©2006 Association for Computing Machinery