Security Issues on Outlier Detection and Countermeasure for Distributed Hierarchical Wireless Sensor Networks

Abstract

Outliers in wireless sensor networks (WSNs) are sensor nodes that launch attacks by abnormal behaviors and fake message dissemination. However, existing cryptographic techniques have difficulty in detecting these outliers, which makes outlier recognition a critical and challenging issue for reliable and secure data dissemination when outliers exist in WSNs. This chapter is concerned about detection and elimination problems of outlier. To efficiently identify and isolate outliers, we present a novel “Outlier Detection and Countermeasure Scheme” (ODCS), which consists of three mechanisms: (1) An abnormal event observation mechanism (AEOM) for network surveillance; (2) An exceptional message supervision mechanism (EMSM) for distinguishing fake messages by exploiting spatiotemporal correlation and consistency; (3) An abnormal frequency supervision mechanism (AFSM) for the evaluation of node behavior. The chapter also provides a heuristic methodology which does not need the knowledge of normal or malicious sensors in advance. This property makes the ODCS not only to distinguish and deal with various dynamic attacks automatically without advance learning but also reduces the requirement of capability for constrained nodes. In our solution, the communication is limited to a local range, such as one-hop or a cluster, which can reduce the communication frequency and circumscribe the session range further. Moreover, the chapter also provides countermeasures for different types of attacks, such as the rerouting scheme and the rekey security scheme, which can separate outliers from normal sensors and enhance the robustness of network, even when some nodes are compromised by adversary. Simulation results indicate that our approach can effectively detect and defend the outlier attack.