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Aiming at the problem of plaintext recovery attack on 3DES algorithm with different byte keys, a plaintext recovery method for 3DES algorithm with different byte keys is proposed. In order to strengthen the anti-attack ability of DES algorithm, 3DES algorithm is used, which includes double-key 3DES of and triple-double-key 3DES. The 3DES algorithm with different seed key length is used to recover the plaintext. The attack algorithm for the first 256-byte plaintext encrypted by 3DES algorithm are given through the single byte deviation rule and double byte deviation rule. The results show that with the increase of the corresponding quantization order, the processing effect of the method is better than that of the chaos mapping method (4.2 dB), and with the increase of the quantization order, the advantage of the method is constantly improved; the attack result of the method on 256 plaintext is shown as the 114th byte to 127th byte from 3072 bytes, so that in practical application, the plaintext encrypted by 3DES algorithm can be recovered better. [ABSTRACT FROM AUTHOR]

Cryptography is the study of techniques which used to transforms the original text (plain text) to cipher text (non understandable text). Due to recent progress on digitized data exchange in electronic way, information security has become crucial in data storage and transmission. Some of the cryptographic algorithm has provided a promising solution which not only protects the data but also authenticates the systems and its participants, so the threat of various attacks is minimized. Nonetheless in the advancement of computing resources the cryptanalysis techniques also emerged and performing competitively in the field of information security with good results. In this paper, we have proposed the optimized deep neural network approach for cryptanalysis of symmetric encryption algorithm 64-bit DES (Data encryption standard). Our approach has used back propagation technique with multiple hidden layers and advanced activation function also we have addressed the problem of vanishing gradient. Further, the implementation results show that we have achieved 90% accuracy which is significantly higher as compared to previous approaches. We have also compared the proposed technique with the existing ones against three parameters i.e. time, loss, accuracy. [ABSTRACT FROM AUTHOR]