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.

翻译：逻辑锁定是一种在委托制造集成电路设计时保护设计安全的前沿技术。近期，基于图神经网络（GNN）的链路预测攻击已被开发出来，这些攻击能够成功破解所有被认为是具有学习鲁棒性的多路选择器型锁定技术。我们提出SimLL——一种基于相似性的新型锁定技术，该技术通过多路选择器锁定设计，并展现出对现有结构利用型无预言机学习攻击的鲁棒性。为混淆机器学习（ML）模型，SimLL在逻辑门或连线之间引入受密钥控制的多路选择器，这些选择器具有高度的拓扑与功能相似性。实验结果表明，SimLL能将现有基于机器学习的攻击准确率降至约50%，使其相较随机猜测几乎无优势。