On Hardware Trojan Detection using Oracle-Guided Circuit Learning
Rajesh Kumar Datta, Guangwei Zhao, Dipali Jain, Kaveh Shamsi · 2024
Hardware Trojans, i.e. malicious circuitry inserted into a design by an untrusted foundry or designer, pose a threat to the fabless semiconductor industry. The detection of hardware Trojans has been the subject of numerous studies over the years. In this paper, we discuss a novel approach to Trojan detection: using the framework of oracle-guided circuit learning (OGCL) or deobfuscation, which has traditionally been used for assessing the security of circuit obfuscation schemes. We show how arbitrary functional Trojan detection can polynomially be reduced to OGCL, yielding a more formal and versatile framework than traditional heuristic techniques. This formulation can also be used to locate Trojans and can be easily extended to side-channel or hybrid detection by using non-functional OGCL. The main challenge with this approach is its worst-case-exponential space complexity when using baseline Boolean satisfiability (SAT)-based circuit deobfuscation. To this end, we propose some novel techniques based on AllSAT, cube generalization, and quantified Boolean Formula (QBF) solving. We present a set of experiments on benchmark circuits to showcase the validity and performance of our framework.