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OMLA: An Oracle-Less Machine Learning-Based Attack on Logic Locking
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DOI：10.1109/TCSII.2021.3113035.png)
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Abstract
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Hardware-based attacks on the semiconductor supply chain are emerging due to the globalization of the design flow. Logic locking is a design-for-trust scheme that promises protection throughout the supply chain. While attacks have heavily relied on an oracle to break logic locking, machine learning (ML)-based attacks demonstrate the daunting possibility of breaking locking even without an oracle. Although very potent, current ML-based attacks recover only a subset of the transformations introduced by locking. We aim to address this shortcoming by developing an oracle-less graph neural network-based attack called OMLA, questioning once again the security of logic locking. Our experiments on ISCAS-85 and ITC-99 benchmarks demonstrate that OMLA achieves a key-prediction accuracy up to 97.22% and outperforms state-of-the-art SnapShot and SAIL attacks for all evaluated benchmarks.
Keywords:
Logic gates
Predictive models
Integrated circuit modeling
Libraries
Encoding
Labeling
Foundries
Logic locking
oracle-less attack
graph neural networks
machine learning
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