Thwarting GNN-Based Attacks Against Logic Locking

The globalization of the IC manufacturing flow has exposed intellectual property (IP) to many untrustworthy entities. As a result, security should be considered a new paradigm in designing circuits to protect the integrity and confidentiality of the IP. Logic locking is a holistic design-for-trust (DFT) technique that can protect circuits against IP piracy and reverse engineering. However, a large body of recent research has demonstrated successful methods of recovering the secret key and restoring the original functionality of existing locking systems. Although SAT attack has been a de facto technique to break the logic locking, the threat model and efficiency of this attack have been questioned recently. To overcome these shortcomings, researchers have proposed powerful structural attacks that break the locked circuits without the need for functionally unlocked circuits (Oracle). Among structural attacks, machine learning (ML)-based attacks are the most potent attacks as they harness the power of neural networks to learn traces of the locking structures and use this knowledge to reverse back and neutralize the locking scheme. Among ML approaches, GNN (graph neural networks)-based attacks are shown to be the most capable tools that attackers can employ as they exploit graph structures inherent to a circuit's netlist. In this paper, (1) We discuss the inherent structural weaknesses of the logic locking techniques. (2) Knowing these weaknesses, we investigate the challenges of protecting circuits against GNN-based attacks. (3) We propose GNN-resilient Interconnect-based obfuscation (GRIN) and GNN-resilient Gate-based Obfuscation (GREGO) logic locking schemes with learning resilient structures. We evaluate our secure schemes using ISCAS-85 and ITC-99 benchmarks and provide comprehensive security and overhead analysis of our proposed schemes.