Project #160018b: Automated and Connected Electric Vehicle Integration- Detection of Trojan Hardware by Using Machine Learning

Electronic systems have advanced to the point that our daily activities depend on them and we trust the Integrated Circuits (IC) within the electronic devices to perform their required operation. Due to current manufacturing trends, ICs are outsourced to third parties, and may cause the integrity of the IC to be compromised. Systems that rely on ICs are then open to attacks; hardware and internal structure of the ICs can be modified, without the knowledge of the designer. The malicious, undesired, intentional modification of an electronic circuit or design, resulting in incorrect operation of the electronic device is called, “Trojan Hardware.” These are considered as a back-door that, when inserted into hardware, can bypass security measures within the system, either software- or hardware-based. This research investigates a machine learning method that relies on the side-channel signature of the IC to detect Trojan Hardware.

Daisy Daisy
Faculty Supervisor: 
Mitra Mirhassani
Partner University: