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A New, Principled Approach to Anomaly Detection

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3 Author(s)
Ferragut, E.M. ; Comput. Sci. & Eng. Div., Oak Ridge Nat. Lab., Oak Ridge, WI, USA ; Laska, J. ; Bridges, R.A.

Intrusion detection is often described as having two main approaches: signature-based and anomaly-based. We argue that only unsupervised methods are suitable for detecting anomalies. However, there has been a tendency in the literature to conflate the notion of an anomaly with the notion of a malicious event. As a result, the methods used to discover anomalies have typically been ad hoc, making it nearly impossible to systematically compare between models or regulate the number of alerts. We propose a new, principled approach to anomaly detection that addresses the main shortcomings of ad hoc approaches. We provide both theoretical and cyber-specific examples to demonstrate the benefits of our more principled approach.

Published in:

Machine Learning and Applications (ICMLA), 2012 11th International Conference on  (Volume:2 )

Date of Conference:

12-15 Dec. 2012