Learning Plaintext-Ciphertext Cryptographic Problems via ANF-based SAT Instance Representation

Neural Information Processing Systems 

Cryptographic problems, operating within binary variable spaces, can be routinely transformed into Boolean Satisfiability (SAT) problems regarding specific cryptographic conditions like plaintext-ciphertext matching.