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ML-Based Framework for Automatically Generating Hardware Trojan Benchmarks

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A new technical paper titled “Automatic Hardware Trojan Insertion using Machine Learning” was published by researchers at University of Florida and Stanford University.

Abstract (partial):
“In this paper, we present MIMIC, a novel AI-guided framework for automatic Trojan insertion, which can create a large 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, extensively evaluated the framework by exploring several use-cases, and quantified its effectiveness to demonstrate highly promising results.”

Find the technical paper here. April 2022.

Authors: Jonathan Cruz, Pravin Gaikwad, Abhishek Nair, Prabuddha Chakraborty, Swarup Bhunia. arXiv:2204.08580v1.

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