ALMOST: Adversarial Learning to Mitigate Oracle-less ML Attacks via Synthesis Tuning

Animesh Basak Chowdhury; Lilas Alrahis; Luca Collini; Johann Knechtel; Ramesh Karri; Siddharth Garg; Ozgur Sinanoglu; Benjamin Tan

arXiv · 2303.03372

ALMOST: Adversarial Learning to Mitigate Oracle-less ML Attacks via Synthesis Tuning

Animesh Basak Chowdhury·Lilas Alrahis·Luca Collini·Johann Knechtel·Ramesh Karri·Siddharth Garg·Ozgur Sinanoglu·Benjamin Tan

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Abstract

Oracle-less machine learning (ML) attacks have broken various logic locking schemes. Regular synthesis, which is tailored for area-power-delay optimization, yields netlists where key-gate localities are vulnerable to learning. Thus, we call for security-aware logic synthesis. We propose ALMOST, a framework for adversarial learning to mitigate oracle-less ML attacks via synthesis tuning. ALMOST uses a simulated-annealing-based synthesis recipe generator, employing adversarially trained models that can predict state-of-the-art attacks' accuracies over wide ranges of recipes and key-gate localities. Experiments on ISCAS benchmarks confirm the attacks' accuracies drops to around 50\% for ALMOST-synthesized circuits, all while not undermining design optimization.

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BibTeX RIS

Animesh Basak Chowdhury, Lilas Alrahis, Luca Collini, Johann Knechtel, Ramesh Karri, Siddharth Garg, Ozgur Sinanoglu, Benjamin Tan. 2023-03-06. ALMOST: Adversarial Learning to Mitigate Oracle-less ML Attacks via Synthesis Tuning. https://arxiv.org/abs/2303.03372

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