Side-channel Analysis for Hardware Trojan Detection using Machine Learning

The evolving trend of the semiconductor supply chain resulted in a wide array of trust issues for electronic hardware. Among them, malicious alteration of designs in an untrusted design house or foundry, also known as hardware Trojan insertion, has emerged as a serious concern. A popular countermeasure against hardware Trojan attacks relies on identifying a Trojan fingerprint in a side - channel parameter. However, side - channel analysis suffers from (1) the process variations introduced in chips during fabrication and (2) the inability of conventional techniques to detect side - channel signatures of a small Trojan in a large design. In this paper, we propose a machine learning approach to detect malicious Trojan activities in a chip with high sensitivity. We use a custom - designed circuit board and measurements from several Trojan-inserted test chips for validating our proposed technique. We were able to detect Trojans with very high confidence and precision. Our method could detect extremely small Trojans of size as small as four gates with over 80% confidence. For larger Trojans, the prediction confidence is above 99%. We have also devised and implemented a framework for time - efficient automatic testing of a target chip using our method.