SAT to SAT-hard clause translator: work-in-progress

Logic obfuscation emerged as an efficient solution to strengthen the security of integrated circuits (ICs) from multiple threats including reverse engineering and intellectual property (IP) theft. Emergence of Boolean Satisfiability (SAT) attacks and its variants have shown to circumvent the security mechanisms such as obfuscation and a plethora of its variants. Considering the size of ICs and the amount of time it takes to validate a defense i.e., obfuscation against SAT attack could range from few ms to days. In contrast, our current work focuses on devising an iterative, dynamic and intelligent SAT-hard clause generator for a given SAT-prone problem. The proposed Machine Learning (ML)-based SAT to unSAT clause translator is a SAT-hard clause generator that utilizes a bipartite propagation based neural network model. The utilized model comprises multiple layers of artificial neural networks to extract the dependencies of literals and variables, followed by long short term memory (LSTM) networks to validate the SAT hardness. The proposed ML-based SAT to unSAT clause translator is trained with conjunctive normal form (CNF) of the IC netlist that are both SAT solvable and SAT-hard. Further, the model is also trained to convert a CNF from satisfiable (SAT) to unsatisfiable (unSAT) form with minor perturbation (which translates to minor overheads) so that the SAT-attack cannot decrypt the keys. To the best of our knowledge, no previous work has been reported on neural network based SAT-hard clause or CNF translator for circuit obfuscation. We evaluate our proposed models's empirical performance against MiniSAT with 300 CNFs.