Adaptive anomaly detection with evolving connectionist systems

Anomaly detection holds great potential for detecting previously unknown attacks. In order to be effective in a practical environment, anomaly detection systems have to be capable of online learning and handling concept drift. In this paper, a new adaptive anomaly detection framework, based on the use of unsupervised evolving connectionist systems, is proposed to address these issues. It is designed to adapt to normal behavior changes while still recognizing anomalies. The evolving connectionist systems learn a subject's behavior in an online, adaptive fashion through efficient local element tuning. Experiments with the KDD Cup 1999 network data and the Windows NT user profiling data show that our adaptive anomaly detection systems, based on Fuzzy Adaptive Resonance Theory (ART) and Evolving Fuzzy Neural Networks (EFuNN), can significantly reduce the false alarm rate while the attack detection rate remains high.