SimLL: Similarity-Based Logic Locking Against Machine Learning Attacks

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...

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Bibliographic Details
Published in2023 60th ACM/IEEE Design Automation Conference (DAC) pp. 1 - 6
Main Authors Chowdhury, Subhajit Dutta, Yang, Kaixin, Nuzzo, Pierluigi
Format Conference Proceeding
LanguageEnglish
Published IEEE 09.07.2023
Subjects
Fabrication
graph neural networks
hardware security
Integrated circuit synthesis
link prediction
Logic gates
Machine learning
Multiplexing
Robustness
Topological similarity
Wires
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Summary: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.
DOI:10.1109/DAC56929.2023.10247822