A Statistical Trust for Detecting Malicious Nodes in IoT Sensor Networks

Abstract

The unattended malicious nodes pose great security threats to the integrity of the IoT sensor networks. However, preventions such as cryptography and authentication are difficult to be deployed in resource constrained IoT sensor nodes with low processing capabilities and short power supply. To tackle these malicious sensor nodes, in this study, the trust computing method is applied into the IoT sensor networks as a light weight security mechanism, and based on the theory of Chebyshev Polynomials for the approximation of time series, the trust data sequence generated by each sensor node is linearized and treated as a time series for malicious node detection. The proposed method is evaluated against existing schemes using several simulations and the results demonstrate that our method can better deal with malicious nodes resulting in higher correct packet delivery rate.