Alert prioritization in Intrusion Detection Systems

Intrusion Detection Systems (IDSs) are designed to monitor user and/or network activity and generate alerts whenever abnormal activities are detected. The number of these alerts can be very large; making the task of security analysts difficult to manage. Furthermore, IDS alert management techniques, such as clustering and correlation, suffer from involving unrelated alerts in their processes and consequently provide imprecise results. In this paper, we propose a fuzzy-logic based technique for scoring and prioritizing alerts generated by an IDS(
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). In addition, we present an alert rescoring technique that leads to a further reduction of the number of alerts. The approach is validated using the 2000 DARPA intrusion detection scenario specific datasets and comparative results between the Snort IDS alert scoring and our scoring and prioritization scheme are presented.