Alarm Reduction and Correlation in Intrusion Detection Systems

Abstract

Large Critical Complex Infrastructures are increasingly dependent on IP networks. Reliability by redundancy and tolerance are an imperative for such dependable networks. In order to achieve the desired reliability, the detection of faults, misuse, and attacks is essential. This can be achieved by applying methods of intrusion detection. However, in large systems, these methods produce an uncontrollable vast amount of data which overwhelms human operators. This paper studies the role of alarm reduction and correlation in existing networks for building more intelligent safeguards that support and complement the decisions by the operator. We present an architecture that incorporates Intrusion Detection Systems as sensors, and provides quantitatively and qualitatively improved alarms to the human operator. Alarm reduction via static and adaptive filtering, aggregation, and correlation is demonstrated using realistic data from sensors such as Snort, Samhain, and Syslog.