A Novel Statistical-Based Method to Detect and Mitigate the Effect of the Black Hole Attack in Vehicular Networks

Abstract

The security of ad-hoc networks remains a challenge for researchers in this digital era. This type of network is vulnerable to attack by dishonest users especially with decentralised and open architecture, making their protection against malicious attacks difficult. Vehicular networks are a particular type of ad-hoc networks, but security threats are inherited from the parent network. Among the best-known attacks, there is the black hole attack with which a malicious user tries to forge the routing process to acquire the route illegally, and subsequently, it intercepts the data packets and deletes them. As a result, the receiver is out of service. In this paper, a black hole attack detection system is proposed, leveraging a statistical normality test. The system operates without requiring modifications to the existing routing protocol. Once the attack is detected, a reaction mechanism is triggered to mitigate its impact and preserve network communication. Simulations conducted by using SUMO and NS-2 demonstrate the effectiveness of the proposed system in mitigating the impact of black hole attacks with minimal overhead.