Security and Communication Networks

Volume 2019, Article ID 9580862, 5 pages

https://doi.org/10.1155/2019/9580862

## Analysis of DES Plaintext Recovery Based on BP Neural Network

^{1}State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China^{2}Information Engineering University, Zhengzhou, China

Correspondence should be addressed to Sijie Fan; moc.qq@841337628

Received 16 May 2019; Accepted 22 October 2019; Published 11 November 2019

Guest Editor: Leonel Sousa

Copyright © 2019 Sijie Fan and Yaqun Zhao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Backpropagation neural network algorithms are one of the most widely used algorithms in the current neural network algorithm. It uses the output error rate to estimate the error rate of the direct front layer of the output layer, so that we can get the error rate of each layer through the layer-by-layer backpropagation. The purpose of this paper is to simulate the decryption process of DES with backpropagation algorithm. By inputting a large number of plaintext and ciphertext pairs, a neural network simulator for the decryption of the target cipher is constructed, and the ciphertext given is decrypted. In this paper, how to modify the backpropagation neural network classifier and apply it to the process of building the regression analysis model is introduced in detail. The experimental results show that the final result of restoring plaintext of the neural network model built in this paper is ideal, and the fitting rate is higher than 90% compared with the true plaintext.

#### 1. Introduction

The study of cryptography mainly includes two aspects, cryptographic design and cryptanalysis. There are independent and mutually unified relationships between them [1]. Block cipher is an important branch of symmetric cryptography. It uses the same key in encryption and decryption and plays a very important role in information and communication security. We hope to use existing cryptanalysis methods to design cryptanalysis methods that can resist all cryptanalysis methods. At the same time, we also hope to use the updated cryptanalysis methods to find some security flaws in cryptanalysis algorithms.

Modern cryptosystems often use methods to expand the key space or increase the complexity of encryption and decryption, and use some mathematical problems as the theoretical basis, which greatly improves the requirement of computational power in cryptographic deciphering. Sometimes the cost of traditional cryptographic deciphering methods may exceed the value of cryptographic deciphering. Artificial neural network (ANN) is the same discipline as cryptography for studying information processing. Neural network algorithm has the characteristics of nonlinear massively parallel-distributed processing and has strong high-speed information processing and uncertainty information processing capability. Using neural networks to solve cryptographic problems will provide a new research idea for cryptography.

In 2008, Bafghi et al. used a recurrent neural network to solve the problem of finding the least-weight multibranch path between two known nodes in the differential operation graph of block cipher. The main idea was to minimize the loss function of the neural network [2]. In 2010, Alallayah et al. considered the black box characteristics of neural networks, combined with system identification technology and adaptive system technology, simulated the neural model of the cryptanalysis target system and could guess the key from a given ciphertext [3]. In 2012, Alani et al. used a new cryptanalysis attack on DES (Data Encryption Standard) and 3DES (Triple Data Encryption Algorithm) cryptographic algorithms. The attack implemented was a known plaintext attack based on a neural network. In this attack, they trained a neural network to retrieve plaintext from ciphertext without retrieving the keys used in encryption. Compared with other attacks, this method reduced the number of known plaintexts required and reduced the time required to perform a full attack [4]. The above methods were less able to directly restore the plaintext sequence, and the experimental procedures of the related literature were mostly based on the simplified cryptographic encryption algorithm and had very high requirements on the computing power.

In this paper, we choose DES algorithm as a case study of block cipher. We propose to use BP (backpropagation) neural network algorithm to simulate the mapping relationship between ciphertext and plaintext. The ciphertext obtained by DES encryption is converted into binary string, which is fed to our improved BP neural network as input after processing according to the preprocessing method defined in this paper. The difference between predicted output and true plaintext is compared for the purpose of cryptanalysis. Compared with previous work, the plaintext recovered by this experiment has a better fitting effect with true plaintext. According to the error rate defined in this paper, the experimental error rate can be controlled below 10%.

The second section of this paper briefly introduces the development history and basic working principle of block cipher. The third section briefly introduces the principle of BP algorithm and the modification we have made to it, thus successfully building a regression model. The fourth section shows our experimental process and results.

#### 2. Brief Introduction to Block Ciphers

Block cipher is one of the important systems in modern cryptography, which is an important part of many cryptosystems. Block cipher usually refers to a kind of cipher algorithm that can only deal with a piece of data of a certain length at a time. Here, the “piece” is called a block. The number of bits in a block is called the block length. Specifically, the principle of block cipher is to divide the plaintext message sequence into a group encrypts it according to a set of fixed encryption algorithms under the control of the key , and outputs a group of ciphertext . The model is shown in Figure 1.