weighting versus pruning in rule validation for detecting network and host anomalies
For intrusion detection, the LERAD algorithm learns a succinct set of comprehensible rules for detecting anomalies, which could be novel attacks. LERAD validates the learned rules on a separate held-out validation set and removes rules that cause false alarms. However, removing rules with possible high coverage can lead to missed detections. We propose to retain these rules and associate weights to them. We present three weighting schemes and our empirical results indicate that, for LERAD, rule weighting can detect more attacks than pruning with minimal computational overhead.
rule weighting anomaly detection machine learning rule pruning