In this paper, we introduce the Satisfiability Modulo Theory (SMT) attack on obfuscated circuits. The proposed attack is the superset of Satisfiability (SAT) attack, with many additional features. It uses one or more theory solvers in addition to its internal SAT solver. For this reason, it is capable of modeling far more complex behaviors and could formulate much stronger attacks. In this paper, we illustrate that the use of theory solvers enables the SMT to carry attacks that are not possible by SAT formulated attacks. As an example of its capabilities, we use the SMT attack to break a recentmore »
NNgSAT: Neural Network guided SAT Attack on Logic Locked Complex Structures
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 more »
- Award ID(s):
- 1718434
- Publication Date:
- NSF-PAR ID:
- 10298702
- Journal Name:
- 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD)
- Sponsoring Org:
- National Science Foundation
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