Applying chaos theory for runtime Hardware Trojan detection

Abstract

Hardware Trojans (HTs) are posing a serious threat to the security of Integrated Circuits (ICs). Detecting HT in an IC is an important but hard problem due to the wide spectrum of HTs and their stealthy nature. In this paper, we propose a runtime Trojan detection approach by applying chaos theory to analyze the nonlinear dynamic characteristics of power consumption of an IC. The observed power dissipation series is embedded into a higher dimensional phase space. Such an embedding transforms the observed data to a new processing space, which provides precise information about the dynamics involved. The feature model is then built in this newly reconstructed phase space. The overhead, which is the main challenge for runtime approaches, is reduced by taking advantage of available thermal sensors in most modern ICs. The proposed model has been tested for publicly-available Trojan benchmarks and simulation results show that the proposed scheme outperforms the state-of-the-art method using temperature tracking in terms of detection rate and computational complexity. More importantly, the proposed model does not make any assumptions about the statistical distribution of power trace and no Trojan-active data is needed, which makes it appropriate for runtime use.