Efficient Detection and Mitigation of Rushing Attacks in VANETs Using RAID: A Novel Intrusion Detection System

Abstract

The vehicle manufacturing hub has evolved over the last decade with the emergence of self-driving vehicles and human-driven vehicles that use the concept of Artificial Intelligence (AI). Vehicular Ad Hoc Network (VANET) is a subset of Mobile Ad Hoc Network (MANET) that allows vehicles to communicate with one another and the Road Side Unit (RSU). VANET has been a game changer with features such as accident prevention, real-time traffic, route predictions, discovering an alternate route, alert notifications, safety, and security. VANET systems are distinguished by their ability to transmit critical safety information in real-time, even when the network's topology is constantly changing. With the lifesaving features of VANET comes a disadvantage that can risk the drivers' security and privacy through various attacks on the network. Intruders can steal data, drop data packets and modify, insert, or delete data when it is transmitted between vehicles. To address the mentioned data communication issues as well as various attacks in the VANET network, the authors propose an Intrusion Detection System (IDS) Rushing Attack Intrusion Detection (RAID), a novel framework that performs the detection of rushing attacks in vehicular networks. According to the performance analysis, the proposed framework RAID meets a wide range of security requirements while requiring less communication and storage. The study's findings were found to be more efficient.