Channel-based Sybil Detection in Industrial Wireless Sensor Networks: a Multi-kernel Approach

Abstract

Industrial Wireless Sensor Networks (IWSNs) integrate various types of sensors to measure and control industrial production. However, the unattended open environment makes IWSNs vulnerable to malicious attacks, such as Sybil attacks, which may degrade the network performance. In addition, multipath distortion, impulse noise and interference effects in the harsh industrial environment may influence the accuracy of attack detection. In this paper, we propose a Sybil detection scheme based on power gain and delay spread analysis by exploiting the spatial variability from their channel responses. Specifically, we utilize channel-vectors to represent the sensor features based on the power gain and delay spread extracted from channel response. Furthermore, we develop a kernel-oriented method to distinguish Sybil attackers from benign sensors by clustering the channel-vectors. In addition, to alleviate the impact of industrial noise and interference effects, we design a multi-kernel based fuzzy c-means method to map the extracted channel-vectors into a new feature space such that the dispersive effects on the channel-vectors can be reduced. We also propose a parameter selection method to optimize the employed kernels. The simulation results show that the proposed multi-kernel scheme can achieve high accuracy in detecting the packets from Sybil attackers, and tolerate the dispersive attenuation and interference effects in the industrial environments.