Distributed PCA-based anomaly detection in wireless sensor networks

Detecting anomalies is an important challenge for intrusion detection and fault diagnosis in wireless sensor networks (WSNs). In this paper, we propose a distributed energy-efficient approach for detecting anomalies in sensed data in a WSN. The anomalies in sensed data can be caused due to compromised or malfunctioning nodes. In the proposed approach, we use distributed principal component analysis (DPCA) and fixed-width clustering (FWC) in order to establish a global normal profile and to detect anomalies. The process of establishing the global normal profile is distributed among all sensor nodes. We also use weighted coefficients and a forgetting curve to periodically update the established normal profile. We demonstrate that the proposed distributed approach achieves comparable accuracy compared to a centralized approach, while the communication overhead in the network and energy consumption is significantly reduced.