Researchers have developed a method to use deep neural networks (DNNs) to test the ciphertext indistinguishability property in post-quantum cryptography (PQC) systems and hybrid encryption schemes. This approach validates the empirical security of PQC KEMs, combiners with classical RSA, and cascade symmetric encryption methods by modeling them as binary classification tasks. The findings confirm that no tested algorithm or combination shows significant vulnerabilities under the DNN model, underscoring the potential for deep learning to enhance practical validation of cryptographic systems.
Read the full article at arXiv cs.CR (Cryptography & Security)
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