Privacy and trustworthiness are the key apprehensions for the users of Internet of Vehicle (IoV) services. Having multiple components involved in the communication (i.e., sensors, vehicles, humans, and infrastructures), the IoV platforms are exposed to a range of attacks. This manuscript will focus on Distributed Denial of Service (DDOS) attacks by adding the design of an Intrusion Detection Systems (IDS) tailored to IoV systems. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) techniques will be investigated that can help in making refined defense architecture for countering DDOS attacks in IoV networks. Furthermore, a fuzzy logic and Q-learning based proposed solution is tested through simulations which argues about the usefulness of the proposed approach in comparison with conventional techniques.
|Number of pages||8|
|Journal||Sustainable Computing: Informatics and Systems|
|Publication status||Published - 25 May 2019|