OMLA: An Oracle-Less Machine Learning-Based Attack on Logic Locking

• Published in: IEEE transactions on circuits and systems. II, Express briefs Vol. 69; no. 3; pp. 1602 - 1606
• Main Authors: Alrahis, Lilas, Patnaik, Satwik, Shafique, Muhammad, Sinanoglu, Ozgur
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Benchmarks; Encoding; Foundries; Globalization; Graph neural networks; Integrated circuit modeling; Labeling; Libraries; Locking; Logic; Logic gates; Logic locking; Machine learning; oracle-less attack; Predictive models; Supply chains
• ISSN: 1549-7747; 1558-3791
• DOI: 10.1109/TCSII.2021.3113035

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.