Anomaly detection in nuclear power plant data using support vector data description

Anomaly detection has drawn a slew of attention in recent years, although term has been known as outlier detection in statistics several decades ago. Everyday large volume of data is being generated. For example, flight navigation data, health care monitoring data, social media data, video surveillance data etc. This data contains rare events or anomalous points that needs to be found out-for example less than 2 % of all visitors who visits Amazon website make a purchase. Thus anomaly detection problem can be interesting due to business perspective, security, maintenance etc. The problem becomes challenging because of noise, heterogeneity, high dimensionality of the data. This paper studies a robust algorithm, based on support vector data description, for anomaly detection. We perform extensive experiments on real data coming from nuclear power plant to empirically demonstrate the effectiveness of the algorithm as well as finding anomalies in the data set. We also discuss extensions of the algorithm to find anomalies in high dimension and non linearly separable data.