  The globalization of the IC supply chain has raised many security threats,
especially when untrusted parties are involved. This has created a demand for a
dependable logic obfuscation solution to combat these threats. Amongst a wide
range of threats and countermeasures on logic obfuscation in the 2010s decade,
the Boolean satisfiability (SAT) attack, or one of its derivatives, could break
almost all state-of-the-art logic obfuscation countermeasures. However, in some
cases, particularly when the logic locked circuits contain complex structures,
such as big multipliers, large routing networks, or big tree structures, the
logic locked circuit is hard-to-be-solved for the SAT attack. Usage of these
structures for obfuscation may lead a strong defense, as many SAT solvers fail
to handle such complexity. However, in this paper, we propose a
neural-network-guided SAT attack (NNgSAT), in which we examine the capability
and effectiveness of a message-passing neural network (MPNN) for solving these
complex structures (SAT-hard instances). In NNgSAT, after being trained as a
classifier to predict SAT/UNSAT on a SAT problem (NN serves as a SAT solver),
the neural network is used to guide/help the actual SAT solver for finding the
SAT assignment(s). By training NN on conjunctive normal forms (CNFs)
corresponded to a dataset of logic locked circuits, as well as fine-tuning the
confidence rate of the NN prediction, our experiments show that NNgSAT could
solve 93.5% of the logic locked circuits containing complex structures within a
reasonable time, while the existing SAT attack cannot proceed the attack flow
in them.
