Neural Cryptanalysis for Cyber-Physical System Ciphers

A key cryptographic research interest is developing an automatic, black-box method to provide a relative security strength measure for symmetric ciphers, particularly for proprietary cyber-physical systems (CPS) and lightweight block ciphers. This thesis work extends the work of the recently-developed neural cryptanalysis method, which trains neural networks on a set of plaintext/ciphertext pairs to extract meaningful bitwise relationships and predict corresponding ciphertexts given a set of plaintexts. As opposed to traditional cryptanalysis, the goal is not key recovery but achieving a mimic accuracy greater than a defined base match rate. In addition to reproducing tests run with the Data Encryption Standard, this work applies neural cryptanalysis to round-reduced versions and components of the SIMON/SPECK family of block ciphers and the Advanced Encryption Standard. This methodology generated a metric able to rank the relative strengths of rounds for each cipher as well as algorithmic components within these ciphers. Given the current neural network suite tested, neural cryptanalysis is best-suited for analyzing components of ciphers rather than full encryption models. If these models are improved, this method presents a promising future in measuring the strength of lightweight symmetric ciphers, particularly for CPS.