Outliers in wireless sensor networks (WSNs) are sensor nodes that issue attacks by abnormal behaviours and fake message dissemination. However, existing cryptographic techniques are hard to detect these inside attacks, which cause outlier recognition a critical and challenging issue for reliable and secure data dissemination in WSNs. To efficiently identify and isolate outliers, this study presents a novel outlier detection and countermeasure scheme (ODCS), which consists of three mechanisms: (i) abnormal event observation mechanism for network surveillance; (ii) exceptional message supervision mechanism for distinguishing fake messages by exploiting spatiotemporal correlation and consistency and (iii) abnormal behaviour supervision mechanism for the evaluation of node behaviour. The ODCS provides a heuristic methodology and does not need the knowledge about 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 to reduce the requirement of capability for constrained nodes. In the ODCS, the communication is limited in a local range, such as one-hop or a cluster, which can reduce the communication frequency and circumscribe the session range further. Moreover, the ODCS 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.
ASJC Scopus subject areas
- Information Systems
- Computer Networks and Communications