Detecting Sybil attacks in wireless sensor networks using UWB ranging-based information. Issue 21 (30th November 2015)

• Record Type: Journal Article
• Authors: Sarigiannidis, Panagiotis; Karapistoli, Eirini; Economides, Anastasios A.
• Is Part Of: Expert systems with applications. Volume 42:Issue 21(2015)
• Page Start: 7560
• Page End: 7572
• Publication Date: 2015-11-30
• Subjects: Wireless sensor networks -- Ultra-wideband (UWB) radio technology -- Rule-based anomaly detection system -- UWB ranging-based Sybil attack detection -- Detection probability analysis; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.05.057
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Development of a novel rule-based Sybil attack detection system for large-scale WSNs. Integration of UWB ranging-features with expert knowledge in the detection process. Development of a defense scheme against direct, simultaneous Sybil attacks. Derivation of a rigorous analytic framework to determine the system performance. Introduction of an accurate simulation environment to validate the detection analysis. 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.