A Time-series Clustering Approach for Sybil Attack Detection in Vehicular Ad hoc Networks

TLDR: A fuzzy time-series clustering based approach that does not require any additional hardware or infrastructure support for Sybil attack detection in vehicular ad hoc networks that leverages the dispersion of vehicle platoons over time in a network and detects Sybil nodes as those which are traveling closely in a cluster for an unusually long time.

Abstract: Sybil attack is a security threat wherein an attacker creates and uses multiple counterfeit identities risking trust and functionality of a peer-to-peer system. Sybil attack in vehicular ad hoc networks is an emergent threat to the services and security of the system. In the highly dynamic environment of vehicular ad hoc networks, due to mobility and density of nodes, it is challenging to detect the nodes that are launching Sybil attack. Existing techniques mostly use additional hardware or complex cryptographic solutions for Sybil attack detection in vehicular ad hoc networks. In this paper, we propose a fuzzy time-series clustering based approach that does not require any additional hardware or infrastructure support for Sybil attack detection in vehicular ad hoc networks. The proposed technique leverages the dispersion of vehicle platoons over time in a network and detects Sybil nodes as those which are traveling closely in a cluster for an unusually long time. Simulation results and analysis show that the approach is able to identify Sybil nodes with very low false positive and false negative rates even under varying intensity of attack.