Anomaly detection in wireless sensor networks via support vector data description with mahalanobis kernels and discriminative adjustment

In the past few years, wireless sensor networks (WSNs) have been increasingly gaining impact in the real world with with various applications such as healthcare, condition monitoring, control networks, etc. Anomaly detection in WSNs is an important aspect of data analysis in order to identify data items which does not conform to an expected pattern or other items in a dataset. This paper describes a anomaly detection method using support vector data description (SVDD) kernelized by Mahalanobis distance with adjusted discriminant threshold. The efficiency of this method is studied over a real data set. Numerical result demonstrates that the proposed approach achieved a high-level of detection accuracy and a low percentage of false alarm rate owing to wise choices of discriminant threshold.