FuzMet: a fuzzy‐logic based alert prioritization engine for intrusion detection systems

Intrusion detection systems (IDSs) are designed to monitor a networked environment and generate alerts whenever abnormal activities are detected. The number of these alerts can be very large, making their evaluation by security analysts a difficult task. Management is complicated by the need to configure the different components of alert evaluation systems. In addition, IDS alert management techniques, such as clustering and correlation, suffer from involving unrelated alerts in their processes and consequently provide results that are inaccurate and difficult to manage. Thus the tuning of an IDS alert management system in order to provide optimal results remains a major challenge, which is further complicated by the large spectrum of potential attacks the system can be subject to. This paper considers the specification and configuration issues of FuzMet, a novel IDS alert management system which employs several metrics and a fuzzy-logic based approach for scoring and prioritizing alerts. In addition, it features an alert rescoring technique that leads to a further reduction in the number of alerts. Comparative results between SNORT scores and FuzMet alert prioritization onto a real attack dataset are presented, along with a simulation-based investigation of the optimal configuration of FuzMet. The results prove the enhanced intrusion detection accuracy brought by our system. Copyright © 2011 John Wiley & Sons, Ltd. (This paper considers the specification and configuration issues of FuzMet, a novel IDS alert management system which employs several metrics and a fuzzy-logic based approach for scoring and prioritizing alerts. It also features an alert rescoring technique that leads to a further reduction in the number of alerts. Comparative results between SNORT scores and FuzMet alert prioritization onto a real attack dataset are presented, along with a simulation-based investigation of the optimal configuration of FuzMet.)