Artificial Intelligence Enhanced Design for Secure Anti-Tamper Embedded Devices

The attack surface on computing devices is becoming very sophisticated.

This trend is driven by two major factors. The first is the rapid increase of the number of connected devices, with more than 50 Billion devices expected to be deployed before 2025. The second factor is the remarkable growth of outsourcing in the hardware supply chain, which has brought about serious challenges in the form of new security attacks, particularly, IC counterfeit and Hardware Trojan insertion.

Compromised hardware products pose serious threats if used in critical infrastructure and military applications. This continuously evolving landscape of security threats calls for an equally effective and adaptive defence mechanisms. This project will develop such a mechanism, which uses machine learning algorithms to achieve a rapid detection of malicious behaviours in an embedded system and intercede to stop a potential attack.