Lightweight Anomaly Detection for Wireless Sensor Networks

Anomaly detection in wireless sensor networks (WSNs) is critical to ensure the quality of senor data, secure monitoring, and reliable detection of interesting and critical events. The main challenge of anomaly detection algorithm in WSNs is identifying anomalies with high accuracy while consuming minimal resource of the network. In this paper two lightweight anomaly detection algorithms LADS and LADQA are proposed for WSNs. Both algorithms utilize the one-class quarter-sphere support vector machine (QSSVM) and convert the linear optimization problem of QSSVM to a sort problem for the reduced computational complexity. Experimental results show that the proposed algorithms can keep the lower computational complexity without reducing the accuracy for anomaly detection, compared to QSSVM.