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• W3206089473 abstract "Logic locking is a circuit obfuscation technique that inserts additional key gates to the original circuit in order to prevent potential threats such as circuit overproduction, piracy, and counterfeiting. The encrypted circuit generates desired outputs only when the correct keys are applied to the key gates. Previous works have identified the vulnerability of logic locking to satisfiability (SAT)-based attacks. However, SAT attacks are unscalable and have limited effectiveness on circuits with SAT-hard structures. To address the above constraints, we propose GALU, the first genetic algorithm-based logic unlocking framework that is parallelizable and significantly faster than the conventional SAT-based counterparts. GALU works by formulating circuit deobfuscation (i.e., identifying the correct keys) as a combinatorial optimization problem and approaches it using genetic algorithms (GAs). We consider key sequences as individuals in distinct populations and propose an adaptive, diversity-guided GA framework consisting of four main steps: circuit fitness evaluation, population selection, crossover, and mutation. In each iteration, the key sequences with high fitness scores are selected and transformed into the offspring key sequences. As a result of evolutionary key searching, GALU is highly scalable, effective, and efficient. To optimize the runtime overhead of logic unlocking, we integrate the design of GALU’s algorithm, software and hardware in a closed loop. In particular, we identify circuit fitness evaluation as the performance bottleneck and employ hardware emulation on programmable hardware for runtime optimization. To this end, GALU framework automatically constructs customized auxiliary circuitry to pipeline the computation in constraints checking, sorting, crossover, and mutation. GALU is the first adaptive and scalable attack framework that provides the flexibility/trade-off between runtime overhead and key usability. This is achieved by producing a group of approximate keys with improving quality over time. We perform a comprehensive evaluation of GALU’s performance on various benchmarks and demonstrate that GALU achieves up to 1089.2× speedup and 4268.6× more energy-efficiency compared to the state-of-the-art SAT attacks for circuit logic unlocking." @default.
• W3206089473 created "2021-10-25" @default.
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• W3206089473 date "2022-02-18" @default.
• W3206089473 modified "2023-09-25" @default.
• W3206089473 title "GALU: A Genetic Algorithm Framework for Logic Unlocking" @default.
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• W3206089473 doi "https://doi.org/10.1145/3491256" @default.
• W3206089473 hasPublicationYear "2022" @default.
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