Network Intrusion Detection and Visualization Using Aggregations in a Cyber Security Data Warehouse

Abstract

The challenge of achieving situational understanding is a limiting factor in effective, timely, and adaptive cyber-security analysis. Anomaly detection fills a critical role in network assessment and trend analysis, both of which underlie the establishment of comprehensive situational understanding. To that end, we propose a cyber security data warehouse implemented as a hierarchical graph of aggregations that captures anomalies at multiple scales. Each node of our proposed graph is a summarization table of cyber event aggregations, and the edges are aggregation operators. The cyber security data warehouse enables domain experts to quickly traverse a multi-scale aggregation space systematically. We describe the architecture of a test bed system and a summary of results on the IEEE VAST 2012 Cyber Forensics data.

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B. Denny Czejdo, E. M. Ferragut, J. R. Goodall and J. Laska, "Network Intrusion Detection and Visualization Using Aggregations in a Cyber Security Data Warehouse," International Journal of Communications, Network and System Sciences, Vol. 5 No. 9A, 2012, pp. 593-602. doi: 10.4236/ijcns.2012.529069.

Conflicts of Interest

The authors declare no conflicts of interest.

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