Log correlation for intrusion detection: a proof of concept

Abstract

Intrusion detection is an important part of networked-systems security protection. Although commercial products exist, finding intrusions has proven to be a difficult task with limitations under current techniques. Therefore, improved techniques are needed. We argue the need for correlating data among different logs to improve intrusion detection systems accuracy. We show how different attacks are reflected in different logs and argue that some attacks are not evident when a single log is analyzed. We present experimental results using anomaly detection for the virus Yaha. Through the use of data mining tools (RIPPER) and correlation among logs we improve the effectiveness of an intrusion detection system while reducing false positives.