Intelligent Intrusion Detection Systems for Connected and Autonomous Vehicles

Connected and Autonomous Vehicles (CAVs) can be vulnerable to various attacks at different levels. The malicious attacks not only result in loss of confidentiality and user privacy, but also lead to more serious consequences such as bodily injury and loss of life. Intrusion detection system (IDS) is an effective way to detect different threats, and trigger alerts to mitigate the attacks. To ensure the safety of CAVs, it is extremely important to detect various attacks accurately in a timely fashion. The goal of the partnership between TELUS and University of Windsor is to investigate and develop intelligent IDS to safeguard CAVs from various security threats by exploiting deep learning. Pursued in close collaboration with TELUS, this project will overcome the core technical challenges, generate novel ideas and techniques for intelligent IDS in CAVs, generate a CAV oriented dataset and develop testbeds to secure CAVs and advance the state-of-art solutions

Faculty Supervisor:

Ning Zhang;Arunita Jaekel

Student:

Partner:

TELUS (Calgary, AB)

Discipline:

Engineering

Sector:

Information and cultural industries

University:

University of Windsor

Program: