A Machine Learning Based Automatic Hardware Trojan Attack Space Exploration and Benchmarking Framework

Due to the current horizontal business model that promotes increasing reliance on untrusted third-party Intellectual Properties (IPs), Computer-Aided-Design (CAD) tools, and design facilities, hidden malicious functionalities, also known as hardware Trojan attacks, have become a serious threat to the semiconductor industry. Development of effective countermeasures against hardware Trojan attacks require: (1) fast and reliable exploration of the viable Trojan attack space for a given design and (2) a suite of high-quality Trojan-inserted benchmarks that meet specific standards. The latter has become essential for the development and evaluation of design/verification solutions to achieve quantifiable assurance against Trojan attacks. While existing static benchmarks provide a baseline for comparing different countermeasures, they only enumerate a limited number of hand-crafted Trojans from the complete Trojan design space. To accomplish these dual objectives, in this paper, we present MIMIC, a novel machine learning guided framework for automatic Trojan insertion, which can create a large and targeted population of valid Trojans for a given design by mimicking the properties of a small set of known Trojans. While there exist tools to automatically insert Trojan instances using fixed Trojan templates, they cannot analyze known Trojan attacks for creating new instances that accurately capture the threat model. MIMIC works in two major steps: (1) it analyzes structural and functional features of existing Trojan populations in a multi-dimensional space to train machine learning models and generate a large number of “virtual Trojans” of the given design, (2) next, it binds them into the design by matching their functional/structural properties with suitable nets of the internal logic structure. We have developed a complete tool flow for MIMIC, evaluated the framework, and quantified its effectiveness to demonstrate highly promising results.