Similarity-Based Logic Locking Against Machine Learning Attacks

Subhajit Dutta Chowdhury; Kaixin Yang; Pierluigi Nuzzo

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arXiv · 2305.05870

Similarity-Based Logic Locking Against Machine Learning Attacks

Subhajit Dutta Chowdhury·Kaixin Yang·Pierluigi Nuzzo

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Abstract

Logic locking is a promising technique for protecting integrated circuit designs while outsourcing their fabrication. Recently, graph neural network (GNN)-based link prediction attacks have been developed which can successfully break all the multiplexer-based locking techniques that were expected to be learning-resilient. We present SimLL, a novel similarity-based locking technique which locks a design using multiplexers and shows robustness against the existing structure-exploiting oracle-less learning-based attacks. Aiming to confuse the machine learning (ML) models, SimLL introduces key-controlled multiplexers between logic gates or wires that exhibit high levels of topological and functional similarity. Empirical results show that SimLL can degrade the accuracy of existing ML-based attacks to approximately 50%, resulting in a negligible advantage over random guessing.

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BibTeX RIS

Subhajit Dutta Chowdhury, Kaixin Yang, Pierluigi Nuzzo. 2023-05-10. Similarity-Based Logic Locking Against Machine Learning Attacks. https://arxiv.org/abs/2305.05870

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