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.

中文翻译：

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基于深度学习的轻量级分组密码分析

大多数传统的密码分析技术通常需要大量时间，已知的明文和内存。本文提出了一种基于深度学习（DL）的通用密码分析模型，该模型试图从已知的明文-密文对中找到分组密码的密钥。通过攻击轻量级分组密码（如简化的DES，Simon和Speck），我们展示了基于DL的密码分析的可行性。结果表明，当密钥空间限制为64个ASCII字符时，基于DL的密码分析可以成功恢复密钥位。传统的密码分析通常在没有密钥空间限制的情况下执行，但是只有Simon和Speck的缩减后轮变体才能成功受到攻击。尽管应用了基于文本的密钥，所提出的基于DL的密码分析可以成功打破Simon32 / 64和Speck32 / 64的所有攻击。结果表明，当密钥空间受到限制时，DL技术可以用作分组密码的密码分析的有用工具。