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. © 2005 IEEE.
|Title of host publication||Proceedings - IEEE International Conference on Data Mining, ICDM|
|Publication status||Published - 2005|