A Time-series Clustering Approach for Sybil Attack Detection in Vehicular Ad hoc Networks
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, the authors 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.