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V. Chandola, A. Banerjee and V. Kumar, “Anomaly Detection: A Survey,” ACM Computing Surveys, Vol. 41, No. 3, 2009, Article 15.

has been cited by the following article:

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

  AUTHORS: Bogdan Denny Czejdo, Erik M. Ferragut, John R. Goodall, Jason Laska

  KEYWORDS: Cyber Security; Network Intrusion; Anomaly Detection; Data Warehouses; Aggregation; Personalization; Situational Understanding

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.5 No.9A, September 18, 2012

  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.