Thesis topic:

Navigating the Emerging AI-based Threat Landscape of Intelligent Vehicles: Cybersecurity Review, Countermeasures, and Challenges

Supervisor: Mubashar Iqbal
- contact: mubashar.iqbal@ut.ee
The integration of artificial intelligence and connectivity has revolutionized the automotive industry, giving rise to intelligent vehicles. However, this technological advancement introduces a complex and evolving cybersecurity threat landscape. This literature review explores the emerging security challenges confronting intelligent vehicles, focusing on vulnerabilities inherent in AI-driven systems, connected infrastructure, and standard vehicular architectures. It systematically identifies and analyzes a range of sophisticated threats, from adversarial machine learning attacks manipulating perception systems to long-range vehicle hijacking. This research synthesizes the current state of proposed mitigation tools and techniques, including intrusion detection systems, cryptographic protocols, and AI-powered defensive mechanisms. The core contribution of this thesis is the development of a comprehensive, evidence-based decision tree. Synthesized from a systematic literature review findings, this structured decision tree is designed to guide automotive stakeholders such as manufacturers, policymakers, and security engineers in systematically identifying, assessing, and selecting appropriate countermeasures for specific cyber threats.