Cybersecurity Enhancement in Autonomous Vehicles Using AI-Based Intrusion Detection Systems

EasyChair Preprint 15381

Abstract

With the growing adoption of autonomous vehicles (AVs), ensuring cybersecurity has become a priority, as vulnerabilities in vehicular networks can lead to severe safety risks. AI-based intrusion detection systems (IDS) provide a robust approach for identifying potential cyber threats, utilizing machine learning algorithms to detect anomalous behavior within AV networks. This paper explores the role of AI-driven IDS in safeguarding autonomous vehicles, examining their architecture, benefits, and limitations. Case studies from real-world AV deployments demonstrate the efficacy of AI-based IDS in detecting and mitigating cybersecurity threats, highlighting their importance in the future of autonomous vehicle technology.

Keyphrases: Artificial Intelligence, Cybersecurity, Intrusion Detection Systems, Threat Detection, Vehicular Networks, autonomous vehicles, machine learning

@booklet{EasyChair:15381,
  author    = {Ethan Roberts},
  title     = {Cybersecurity Enhancement in Autonomous Vehicles Using AI-Based Intrusion Detection Systems},
  howpublished = {EasyChair Preprint 15381},
  year      = {EasyChair, 2024}}