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The ability to detect unexpected events in large networks can be a significant benefit to daily network operations. A great deal of work has been done over the past decade to develop effective anomaly detection tools, but they remain virtually unused in live network operations due to an unacceptably high false alarm rate. In this paper, the authors seek to improve the ability to accurately detect unexpected network events through the use of BasisDetect, a flexible but precise modeling framework. Using a small dataset with labeled anomalies, the BasisDetect framework allows one to define large classes of anomalies and detect them in different types of network data, both from single sources and from multiple, potentially diverse sources.
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