Post-quantum cryptographic algorithm identification using machine learning

Bruno Santos Rocha, Jose Antonio Moreira Xexeo, Renato Hidaka Torres


This research presents a study on the identification of post-quantum cryptography algorithms through machine learning techniques. Plain text files were encoded by four post-quantum algorithms, participating in NIST's post-quantum cryptography standardization contest, in ECB mode. The resulting cryptograms were submitted to the NIST Statistical Test Suite to enable the creation of metadata files. These files provide information for six data mining algorithms to identify the cryptographic algorithm used for encryption. Identification performance was evaluated in samples of different sizes. The successful identification of each machine learning algorithm is higher than a probabilistic bid, with hit rates ranging between 73 and 100%.


Identification of cryptographic algorithm; Data mining; machine learning; post-quantum cryptography, NIST randomness tests

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