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Learning with Enumeration: Neural-Guided SAT Framework for Cryptographic Key Recovery
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Proposes a neural-guided SAT framework for cryptographic key recovery that uses learned guidance to steer enumeration over CNF/ANF encodings of ciphers.
Problem
SAT solvers, including neural-enhanced variants, degrade on cryptographic instances due to structural symmetry, many intermediate variables, and complex algebraic dependencies, hurting cipher key recovery.
Approach
Combines learned neural guidance with enumeration inside a SAT-based pipeline for key recovery, rather than relying on end-to-end neural solving or heuristic solver tuning. Details are not available beyond the abstract.
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