Detecting computer intrusions with Bayesian networks
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
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This paper presents an application of Bayesian networks to the process of intrusion detection in computer networks. The presented system, called Basset (Bayesian System for Intrusion Detection) extends functionality of Snort, an open-source NIDS, by incorporating Bayesian networks as additional processing stages. The flexible nature of this solution allows it to be used both for misuse-based and anomaly-based detection process; this paper concentrates on the misuse-based detection. The ultimate goal is to provide better detection capabilities and less chance of false alarms by creating a platform capable of evaluating Snort alerts in a broader context – other alerts and network traffic in general. An ability to include on-demand information from third party programs is also an important feature of the presented approach to intrusion detection.