Deep Learning-Based Cryptanalysis of Lightweight Block Ciphers

Abstract

Most of the traditional cryptanalytic technologies often require a great amount of time, known plaintexts, and memory. This paper proposes a generic cryptanalysis model based on deep learning (DL), where the model tries to find the key of block ciphers from known plaintext-ciphertext pairs. We show the feasibility of the DL-based cryptanalysis by attacking on lightweight block ciphers such as simplified DES, Simon, and Speck. The results show that the DL-based cryptanalysis can successfully recover the key bits when the keyspace is restricted to 64 ASCII characters. The traditional cryptanalysis is generally performed without the keyspace restriction, but only reduced-round variants of Simon and Speck are successfully attacked. Although a text-based key is applied, the proposed DL-based cryptanalysis can successfully break the full rounds of Simon32/64 and Speck32/64. The results indicate that the DL technology can be a useful tool for the cryptanalysis of block ciphers when the keyspace is restricted.