LCSS Based Sybil Attack Detection and Avoidance in Clustered Vehicular Networks

Future Road transportation mainly depends upon connected vehicles. Intelligent Transportation Systems bring benefits to the road users through Vehicular Adhoc Networks (VANETs). Since VANET packet contains life critical information, security is inevitable. A rogue node called sybil node can transmit fake messages to its neighbours and disrupt the system, challenging security. Since the nodes are very dynamic, stability is also a major concern. Existing rogue node detection methods do not address this problem suitably. In the proposed work, rouge node detection is implemented in a clustered network which improves the stability of the network. The main aim of this paper is to implement a sybil attack detection method in distributed or coordinated clustered networks using a novel hybridization technique. The cluster head detects the sybil attacker by comparing the received signal strength of packets from each node based on a similarity algorithm, Longest Common SubSequence (LCSS). However, if the sybil attacker launches a power control mechanism, the similarity calculation fails. To overcome this issue, a Change Point Detection(CPD) technique by comparing the changes in mean value of RSS time series from a particular node is proposed. Coordinated attacks can be easily detected in a clustered network as the information regarding the attackers' spreads in the network quickly so that the nodes can avoid connecting to such malicious nodes during their journey. The proposed algorithm shows significant improvement in detection rate, detection delay and false positive for varying vehicle count compared to existing techniques.