Power Profiling of Microcontroller's Instruction Set for Runtime Hardware Trojans Detection without Golden Circuit Models
Faiq Khalid Lodhi1,a, Syed Rafay Hasan2, Osman Hasan1,b and Falah Awwadl3
1School of Electrical Engineering
afaiq.khalid@seecs.nust.edu.pk
bosman.hasan@seecs.nust.edu.pk
2Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN, USA.
shasan@tntech.edu
3College of Engineering, United Arab Emirates University, Al-Ain, UAE.
f_awwad@uaeu.ac.ae
ABSTRACT
Globalization trends in integrated circuit (IC) design are leading to increased vulnerability of ICs against hardware Trojans (HT). Recently, several side channel parameters based techniques have been developed to detect these hardware Trojans that require golden circuit as a reference model, but due to the widespread usage of IPs, most of the system-on-chip (SoC) do not have a golden reference. Hardware Trojans in intellectual property (IP)-based SoC designs are considered as major concern for future integrated circuits. Most of the state-of-the-art runtime hardware Trojan detection techniques presume that Trojans will lead to anomaly in the SoC integration units. In this paper, we argue that an intelligent intruder may intrude the IP-based SoC without disturbing the normal SoC operation or violating any protocols. To overcome this limitation, we propose a methodology to extract the power profile of the micro-controllers instruction sets, which is in turn used to train a machine learning algorithm. In this technique, the power profile is obtained by extracting the power behavior of the micro-controllers for different assembly language instructions. This trained model is then embedded into the integrated circuits at the SoC integration level, which classifies the power profile during runtime to detect the intrusions. We applied our proposed technique on MC8051 micro-controller in VHDL, obtained the power profile of its instruction set and then applied deep learning, k-NN, decision tree and naive Bayesian based machine learning tools to train the models. The cross validation comparison of these learning algorithm, when applied to MC8051 Trojan benchmarks, shows that we can achieve 87% to 99% accuracy. To the best of our knowledge, this is the first work in which the power profile of a microprocessor's instruction set is used in conjunction with machine learning for runtime HT detection.
Keywords: Power profiling, Machine learning, Hardware trojans, Runtime detection, Microcontroller, Assembly language instructions.
