DTMS: A Dual Trust-based Multi-level Sybil Attack Detection Approach in WSNs

Abstract

Sensor network has been proven a promising technology for discerning data as well as details about the physical world. The sensor nodes are organized and positioned in an inaccessible and hostile environment and are consequently susceptible to various security attacks. Among the multiple attacks possible in any WSN, the Sybil attack is the most severe kind of attack. Detection of a Sybil node is a complex as well as challenging problem since it illegitimately uses multiple fraudulent identities to misguide and collapse the network. The paper proposes a dual trust-based multi-level Sybil (DTMS) attack detection approach in WSNs. The work engages a multi-level detection system grounded on verification of the node’s identity and location. Later, at each level, CM, CH, and BS, the trust value is calculated. For a satisfactory level of trust value, communication trust is calculated together with data trust. The trust function of DTMS involves dynamic reward and penalty coefficient to ensure severity. Besides, data aggregation is employed, which contributes to lesser communication overhead and energy consumption. The performance evaluation of DTMS is measured in terms of severity of trust function, true detection rate, false detection rate, residual energy, network lifetime, and packet loss ratio. The simulation results validate that DTMS can efficaciously detect Sybil nodes. Experiment results show that the proposed algorithm can detect [[EQUATION]] of Sybil nodes in the malicious environment. Additionally, the DTMS is compared with recent existing schemes in terms of various parameters (True Detection Rate, False Detection Rate, energy consumption, packet loss rate, number of alive nodes, etc.) which show that the DTMS performs desirably.