Discerning Limitations of GNN-based Attacks on Logic Locking
- Resource Type
- Conference
- Authors
- Darjani, Armin; Kavand, Nima; Rai, Shubham; Kumar, Akash
- Source
- 2023 60th ACM/IEEE Design Automation Conference (DAC) Design Automation Conference (DAC), 2023 60th ACM/IEEE. :1-6 Jul, 2023
- Subject
- Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Design automation
Machine learning
Graph neural networks
Logic locking
Structural attacks
ML-based attacks
GNN
- Language
Machine learning (ML)-based attacks have revealed the possibility of utilizing neural networks to break locked circuits without needing functional chips (Oracle). Among ML approaches, GNN (graph neural networks)-based attacks are the most potent tools that attackers can employ as they exploit graph structures inherent to a circuit’s netlist. Although promising, in this paper, we reveal that GNNs have some impediments in attacking locked circuits. We investigate the limits of the state-of-the-art GNN-based attacks against logic locking and show that we can drastically decrease the accuracy of these attacks by utilizing these limitations in the locking process.