Detecting Sybil attacks in wireless sensor networks using UWB ranging-based information

Abstract

Security is becoming a major concern for many mission-critical applications wireless sensor networks (WSNs) are envisaged to support. The inherently vulnerable characteristics of WSNs appoint them susceptible to various types of attacks. This work restrains its focus on how to defend against a particularly harmful form of attack, the Sybil attack. Sybil attacks can severely deteriorate the network performance and compromise the security by disrupting many networking protocols. This paper presents a rule-based anomaly detection system, called RADS, which monitors and timely detects Sybil attacks in large-scale WSNs. At its core, the proposed expert system relies on an ultra-wideband (UWB) ranging-based detection algorithm that operates in a distributed manner requiring no cooperation or information sharing between the sensor nodes in order to perform the anomaly detection tasks. The feasibility of the proposed approach is proven analytically, while the performance of RADS in exposing Sybil attacks is extensively assessed both mathematically and numerically. The obtained results demonstrate that RADS achieves high detection accuracy and low false alarm rate appointing it a promising ADS candidate for this class of wireless networks.