| Title | GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Locking |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Alrahis, Lilas, Patnaik, Satwik, Khalid, Faiq, Hanif, Muhammad Abdullah, Saleh, Hani, Shafique, Muhammad, Sinanoglu, Ozgur |
| Conference Name | 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) |
| Keywords | Benchmark testing, composability, graph neural networks, intellectual property, ip protection, Logic gates, logic locking, machine learning, Network topology, Oracle-less attack, policy-based governance, Prediction algorithms, pubcrawl, resilience, Resiliency, Supply chains, Syntactics |
| Abstract | Logic locking is a holistic design-for-trust technique that aims to protect the design intellectual property (IP) from untrustworthy entities throughout the supply chain. Functional and structural analysis-based attacks successfully circumvent state-of-the-art, provably secure logic locking (PSLL) techniques. However, such attacks are not holistic and target specific implementations of PSLL. Automating the detection and subsequent removal of protection logic added by PSLL while accounting for all possible variations is an open research problem. In this paper, we propose GNNUnlock, the first-of-its-kind oracle-less machine learning-based attack on PSLL that can identify any desired protection logic without focusing on a specific syntactic topology. The key is to leverage a well-trained graph neural network (GNN) to identify all the gates in a given locked netlist that belong to the targeted protection logic, without requiring an oracle. This approach fits perfectly with the targeted problem since a circuit is a graph with an inherent structure and the protection logic is a sub-graph of nodes (gates) with specific and common characteristics. GNNs are powerful in capturing the nodes' neighborhood properties, facilitating the detection of the protection logic. To rectify any misclassifications induced by the GNN, we additionally propose a connectivity analysis-based post-processing algorithm to successfully remove the predicted protection logic, thereby retrieving the original design. Our extensive experimental evaluation demonstrates that GNNUnlock is 99.24% - 100% successful in breaking various benchmarks locked using stripped-functionality logic locking [1], tenacious and traceless logic locking [2], and Anti-SAT [3]. Our proposed post-processing enhances the detection accuracy, reaching 100% for all of our tested locked benchmarks. Analysis of the results corroborates that GNNUnlock is powerful enough to break the considered schemes under different parameters, synthesis settings, and technology nodes. The evaluation further shows that GNNUnlock successfully breaks corner cases where even the most advanced state-of-the-art attacks [4], [5] fail. We also open source our attack framework [6]. |
| DOI | 10.23919/DATE51398.2021.9474039 |
| Citation Key | alrahis_gnnunlock_2021 |
GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Locking