A Side Channel Delay Analysis for Hardware Trojan Detection

Abstract

This research proposal introduces a learning assisted modeling technique for the purpose of Hardware Trojan detection. Our proposed model, unlike the prior art, does not require a Golden fabricated chip as a fingerprint to compare the side channel signals. Instead, by modeling the voltage drop and voltage noise pre-fabrication, and with training a Neural Network post-fabrication, our proposed technique can improve the timing model collected during timing closure and produces a Neural assisted Golden Timing Model (NGTM) for side channel delay-signal analysis. The Neural Network acts as a process tracking watchdog for correlating the static timing data (produced at design time) to the delay information obtained from clock frequency sweeping test. Proposed modeling technique enables Hardware Trojan detection close to 90% in the simulated scenarios.