Machine Learning-Based Security Evaluation and Overhead Analysis of Logic Locking

Piracy and overproduction of hardware intellectual properties are growing concerns for the semiconductor industry under the fabless paradigm. Although chip designers have attempted to secure their designs against these threats by means of logic locking and obfuscation, due to the increasing number of powerful oracle-guided attacks, they are facing an ever-increasing challenge in evaluating the security of their designs and their associated overhead. Especially while many so-called “provable” logic locking techniques are subjected to a novel attack surface, overcoming these attacks may impose a huge overhead on the circuit. Thus, in this paper, after investigating the shortcomings of state-of-the-art graph neural network models in logic locking and refuting the use of hamming distance as a proper key accuracy metric, we employ two machine learning models, a decision tree to predict the security degree of the locked benchmarks and a convolutional neural network to assign a low-overhead and secure locking scheme to a given circuit. We first build multi-label datasets by running different attacks on locked benchmarks with existing logic locking methods to evaluate the security and compute the imposed area overhead. Then, we design and train a decision tree model to learn the features of the created dataset and predict the security degree of each given locked circuit. Furthermore, we utilize a convolutional neural network model to extract more features, obtain higher accuracy, and consider overhead. Then, we put our trained models to the test against different unseen benchmarks. The experimental results reveal that the convolutional neural network model has a higher capability for extracting features from unseen, large datasets which comes in handy in assigning secure and low-overhead logic locking to a given netlist.