Network intrusions compromise the network’s confidentiality, integrity and availability of resources. Intrusion detection systems (IDSs) have been implemented to prevent the problem. Although IDS technologies are promising, their ability of detecting true alerts is far from being perfect. One problem is that of producing large numbers of false alerts, which are termed as malicious by the IDS. In this paper we propose a set of metrics for evaluating the IDS alerts. The metrics will identify false, low-level and redundant alerts by mapping alerts on a vulnerability database and calculating their impact. The metrics are calculated using a metric tool that we developed. We validated the metrics using Weyuker’s properties and Kaner’s framework. The metrics can be considered as mathematically valid since they satisfied seven of the nine Weyuker’s properties. In addition, they can be considered as workable since they satisfied all the evaluation questions from Kaner’s framework.
An alert correlation is a high-level alert evaluation technique for managing large volumes of irrelevant and redundant intrusion alerts raised by Intrusion Detection Systems (IDSs).Recent trends show that pure intrusion detection no longer can satisfy the security needs of organizations. One problem with existing alert correlation techniques is that they group related alerts together without putting their severity into consideration. This paper proposes a novel alert correlation technique that can filter unnecessary and low impact alerts from a large volume of intrusion. The proposed technique is based on a supervised feature selection method that usesclass type to define the correlation between alerts. Alerts of similar class type are identified using a class label. Class types are further classified based on their metric ranks of low, medium and high level. Findings show that the technique is able detect and report high level intrusions.
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