System Level Hardware Trojan Detection Using Side-Channel Power Analysis and Machine Learning

Cyber physical systems (CPS) is a dominant technology in today's world due to its vast variety of applications. But in recent times, the alarmingly increasing breach of privacy and security in CPS is a matter of grave concern. Security and trust of CPS has become the need of the hour. Hardware Trojans are one such a malicious attack which compromises on the security of the CPS by changing its functionality or denial of services or leaking important information. This paper proposes the detection of Hardware Trojans at the system level in AES-256 decryption algorithm implemented in Atmel XMega Controller (Target Board) using a combination of side-channel power analysis and machine learning. Power analysis is done with help of ChipWhisperer-Lite board. The power traces of the golden algorithm (Hardware Trojan free) and Hardware Trojan infected algorithms are obtained and used to train the machine learning model using the 80/20 rule. The proposed machine learning model obtained an accuracy of 97%-100% for all the Trojans inserted.