Research on Multi-class CUSUM Algorithm for Anomaly Detection of WSN

Abstract

Security is one of important research issues in wireless sensor networks (WSN) applications. Given that the single detection threshold of the cumulative sum (CUSUM) algorithm causes longer detection delays and a lower detection rate, a multi-class CUSUM algorithm is hereby proposed, wherein CUSUM algorithms of different thresholds, all of which are selected according to the mean of traffic sequences, are applied to detect anomalous nodes. This study aims to optimize threshold parameters, the size of which increases with the number of traffic sequence. Different values of network traffic sequence were generated and simulated by the NS2 tool. Based on these values, the detection rates of the CUSUM algorithm and multi-class CUSUM algorithms, as well as their false positive rates, are then evaluated. Results show that the proposed algorithm achieves a higher and more accurate rate of detection and lower false positive rates than do the current important intrusion detection schemes of WSN.