Applying Chaos Theory for Runtime Hardware Trojan Monitoring and Detection

Hardware Trojans (HTs) pose a serious threat to the security of Integrated Circuits (ICs). Detecting HTs in an IC is an important but difficult problem due to the wide spectrum of HTs and their stealthy nature. In this paper, we propose a hardware-based runtime detection model that overcomes the existing constraints. It applies chaos theory, which has been shown to be effective in several other domains, to characterize dynamic data in a reconstructed phase space, which helps us describe, analyze, and interpret power consumption data. The proposed chaos based approach does not make any assumption on the statistical distribution of power consumption, this makes our model applicable for runtime use given the fact that power consumption is very dynamic as well as heavily application and data dependent. Hardware overhead, which is the main challenge for runtime approaches, is reduced by taking advantage of available thermal sensors present in most modern ICs. For real world implementation, thermal sensor noise cancelation is considered in our proposed model. Our simulation results for detecting Trojans on publicly available Trojan benchmarks demonstrate that the proposed model outperforms the current runtime Trojan detection approaches in terms of detection rate, computational complexity, and implementation feasibility.