Artificial Intelligence techniques to mitigate cyber-attacks within vehicular networks: Survey. (December 2022)

• Record Type: Journal Article
• Authors: Haddaji, Achref; Ayed, Samiha; Fourati, Lamia Chaari
• Is Part Of: Computers & electrical engineering. Volume 104:Part B(2022)
• Publication Date: 2022-12
• Subjects: Vehicular networks -- Artificial Intelligence -- Machine learning -- Security -- Cyber-attack; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108460
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Rapid advancements in communication technology have made vehicular networks a reality with numerous applications. However, vehicular network security is still an open research problem. Artificial intelligence (AI) techniques have emerged to address these issues. AI and its variants are becoming more popular for detecting attacks and dealing with many types of security issues in vehicular networks. This paper presents a comprehensive survey of AI-based techniques for security issues in vehicular networks. We first give a background on vehicular networks and their vulnerabilities. In addition, assess AI fundamentals with their impact on vehicular security. Furthermore, we classify and compare the AI-based solutions related to security for vehicular networks by proposing a new taxonomy. Finally, we present an analysis of the works included in this survey.