Anomaly Detection Through a Bayesian Support Vector Machine

This paper investigates the use of a one-class support vector machine algorithm to detect the onset of system anomalies, and trend output classification probabilities, as a way to monitor the health of a system. In the absence of "unhealthy" (negative class) information, a marginal kernel density estimate of the "healthy" (positive class) distribution is used to construct an estimate of the negative class. The output of the one-class support vector classifier is calibrated to posterior probabilities by fitting a logistic distribution to the support vector predictor model in an effort to manage false alarms.

• Type: Journal
• Year: 2010
• Where: TR
• Authors: Vasilis A. Sotiris, Peter W. Tse, Michael Pecht
• Keywords: Hardware, Negative Class, One-class Support Vector, Support Vector, TR 2010