Behavior profiling of power distribution networks for runtime hardware trojan detection

Runtime hardware Trojan detection techniques are required in third party IP based SoCs as a last line of defense. Traditional techniques rely on golden data model or exotic signal processing techniques such as utilizing Choas theory or machine learning. Due to cumbersome implementation of such techniques, it is highly impractical to embed them on the hardware, which is a requirement in some mission critical applications. In this paper, we propose a methodology that generates a digital power profile during the manufacturing test phase of the circuit under test. A simple processing mechanism, which requires minimal computation of measured power signals, is proposed. For the proof of concept, we have applied the proposed methodology on a classical Advanced Encryption Standard circuit with 21 available Trojans. The experimental results show that the proposed methodology is able to detect 75% of the intrusions with the potential of implementing the detection mechanism on-chip with minimal overhead compared to the state-of-the-art techniques.