A Cognitive SAT to SAT-Hard Clause Translation-based Logic Obfuscation

Logic obfuscation is introduced as a pivotal defense mechanism against emerging hardware threats on Integrated Circuits (ICs) such as reverse engineering (RE) and intellectual property (IP) theft. The effectiveness of logic obfuscation is challenged by recently introduced Boolean satisfiability (SAT) attack and it's variants. A plethora of counter measures have been proposed to thwart the SAT attacks. Irrespective of the implemented defenses, large power, performance and area (PPA) overheads are seen to be indispensable. In contrast, we propose a neural network-based cognitive SAT to SAT-hard clause translator under the constraints of minimal PPA overheads while preserving the original functionality with impenetrable security. Our proposed method is incubated with a SAT-hard clause generator that translates the existing conjunctive normal form (CNF) through minimal perturbations such as inclusion of pair of inverters or buffers or adding new lightweight SAT-hard block depending on the provided CNF. For efficient SAT-hard clause generation, the proposed method is equipped with a multi-layer neural network that first learns the dependencies of features (literals and clauses), followed by a long-short-term-memory (LSTM) network to validate and backpropagate the SAT-hardness for better learning and translation. For a fair comparison with the state-of-the-art, we evaluate our proposed technique on ISCAS'85 benchmarks. It is seen to successfully defend against multiple state-of-the-art SAT attacks devised for hardware RE. In addition, we also evaluate our proposed technique's empirical performance against MiniSAT, Lingeling and Glucose SAT solvers that form the base for numerous existing deobfuscation SAT attacks.