Adversarial Cyber-Attacks and Defences for In-Vehicle Networks

Project description

A lot of recent automobiles have adopted AI techniques to provide autonomous and safe driving. However, there are cybersecurity attacks on intelligent vehicles and their infrastructure and they have become a big risk to the automobile industry and customers.

The main goal of this research project is to develop adversarial attacks and defences for in-vehicle networks. Machine Learning (ML) techniques (including deep learning techniques) have been applied to intrusion detection for in-vehicle networks. The main function of ML-based intrusion detection is to monitor, identify cyber attacks to in-vehicle networks, in particular, CAN bus. Adversarial attacks are critical security threats against deployed ML- based intrusion detection for in-vehicle networks. In this research, existing adversarial attacks will be tested against offline and deployed ML-based intrusion detection for in-vehicle networks and also novel adversarial attacks will be developed to evaluate the robustness of ML-based intrusion detection. In addition, defences against adversarial attacks will be developed against adversarial attacks and their effectiveness will be assessed via multiple metrics.

Outcomes

• Literature survey on cyber attacks and defences on ML-based intrusion detection for in-vehicle networks.
• Novel adversarial attacks to ML-based intrusion detection for in-vehicle networks.
• Defences against adversarial attacks and their evaluation in terms of robustness and interpretation.

Essential capabilities

Strong background in computer science/IT/software engineering/other related discipline.

Desireable capabilities

machine learning, AI, cybersecurity.

Expected qualifications (Course/Degrees etc.)

BS/BE(honours) or Master.

Candidate Discipline

cybersecurity machine learning artificial intelligence vehicle networks.