Similarity-Based Logic Locking Against Machine Learning Attacks

TL;DR

SimLL is a new logic locking method that uses similarity-based multiplexers to protect circuit designs, effectively resisting recent machine learning-based attacks and reducing their success rate to near random guessing.

Contribution

Introduces SimLL, a novel similarity-based logic locking technique that enhances robustness against GNN-based link prediction attacks.

Findings

SimLL reduces attack accuracy to about 50%.

It effectively confuses ML models exploiting topological similarities.

SimLL maintains security against structure-exploiting oracle-less attacks.

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.