Genetic Algorithm Optimized SVM for DoS Attack Detection in VANETs

A VANET is a collection of wireless vehicle nodes that may connect with one another without the need of fixed infrastructure or centralized management. Vehicular ad hoc networks (VANETs) function in a dynamic and unpredictably changing environment that brings numerous potential security risks. One of the types of attacks that affect VANETs the most is the Denial of Service (DoS) attack. Additionally, VANETs can't be secured by following the conventional approaches to protecting wired or wireless networks because of the ever changing network topology. Because preventative methods are insufficient, using an intrusion detection system (IDS) is crucial to the VANET's defense. In this paper, a new intrusion detection system has been proposed by using an Artificial Neural Network-Fitness function based Genetic Algorithm and Support Vector Machines (SVM) for vehicular ad hoc networks to detect the denial-of-service attack.