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- W4323572055 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." @default.
- W4323572055 created "2023-03-09" @default.
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- W4323572055 date "2023-03-06" @default.
- W4323572055 modified "2023-09-23" @default.
- W4323572055 title "ALMOST: Adversarial Learning to Mitigate Oracle-less ML Attacks via Synthesis Tuning" @default.
- W4323572055 doi "https://doi.org/10.48550/arxiv.2303.03372" @default.
- W4323572055 hasPublicationYear "2023" @default.
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