Detecting Network Anomalies Using CUSUM and EM Clustering
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Alert correlation in collaborative intelligent intrusion detection systems-A survey
Applied Soft Computing
A survey of security visualization for computer network logs
Security and Communication Networks
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Network attacks have become the fundamental threat to today's largely interconnected computer systems. Unauthorized activities and unauthorized access account for a large proportion of these networks. Unauthorized accesses and misuse of critical data can be catastrophic to businesses, emergency services, and even threaten the defense and security of a nation. Intrusion detection system (IDS) is indispensable to defend the system in the face of increasing vulnerabilities. This paper proposes a hybrid intrusion detection and visualization system that leverages the advantages of current signature-based and anomaly detection methods. The hybrid instruction detection system deploys these two methods in a twostaged manner to identify both known and novel attacks. When intrusion is detected, autonomous agents that reside on the system will automatically take actions against misuse and abuse of computer system, thus protecting the system from internal and external attacks.