Research on Plaintext Restoration of AES Based on Neural Network

Hu, Xinyi; Zhao, Yongqiang

doi:10.1155/2018/6868506

Cited by 19 publications

(16 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e number of differences is set as t � 3, and the threshold is set as δ > 0.5. e training sample size is 2 24 , and the testing sample size is 2 18 . e positive and negative samples in training set and testing set account for 1/2 of them, respectively.…”

Section: Results Of the Proposed Distinguishersmentioning

confidence: 99%

See 1 more Smart Citation

Multiple Differential Distinguisher of SIMECK32/64 Based on Deep Learning

Wang

Tian

Zhang

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

Currently, deep learning has provided an important means to solve problems in various fields. Intelligent computing will bring a new solution to the security analysis of lightweight block cipher as its analysis becomes more and more intelligent and automatic. In this study, the novel multiple differential distinguishers of round-reduced SIMECK32/64 based on deep learning are proposed. Two kinds of SIMECK32/64’s 6–11 rounds deep learning distinguishers are designed by using the neural network to simulate the case of the multiple input differences and multiple output differences in multiple differential cryptanalysis. The general models of the two distinguishers and the neural network structures are presented. The random multiple ciphertext pairs and the associated multiple ciphertext pairs are exploited as the input of the model. The generation method of the data set is given. The performance of the two proposed distinguishers is compared. The experimental results confirm that the proposed distinguishers have higher accuracy and rounds than the distinguisher with a single difference. The relationship between the quantity of multiple differences and the performance of the distinguishers is also verified. The differential distinguisher based on deep learning needs less time complexity and data complexity than the traditional distinguisher. The accuracy of filtering error ciphertext of our 8-round neural distinguisher is up to 96.10%.

show abstract

Section: Results Of the Proposed Distinguishersmentioning

confidence: 99%

“…e authors in reference [23] evaluated the various relations between deep learning and cryptography and proposed some possible research directions of using deep learning in cryptanalysis. A feedforward neural network (FNN) was developed in reference [24], which can find plaintext from the ciphertext of AES without using key information.…”

Section: Related Workmentioning

confidence: 99%

Multiple Differential Distinguisher of SIMECK32/64 Based on Deep Learning

Wang

Tian

Zhang

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…The Half-Byte algorithm utilizes four left bits that are often the same sequentially especially in text files. For example in a text file containing the words "fetch", in hexadecimal and binary characters are translated as [7]: Compression with a half byte algorithm. The compression algorithm is: 1.…”

Section: Half-byte Algorithmmentioning

confidence: 99%

“…Thus, the required storage space capacity will be smaller and the time and cost of access becomes smaller as well [5]. Data compressing methods have evolved and each method has advantages and disadvantages, the capabilities of each method of data compressing are generally measured by parameters of size or capacity and compression speed, by compressing or shrinking the file size first, then the required storage capacity becomes smaller [7].…”

Section: Introductionmentioning

confidence: 99%

File Compression Application Design Using Half Byte Algorithm

Hasugian

Manullang²

2021

Info Sains

View full text Add to dashboard Cite

The need for large data storage capacity seems increasingly important. This need is caused by the data that must be stored more and more. The storage is not allocated in one place only. But they will also store data or files elsewhere as data backups. How much storage capacity must be provided to hold all the data. Half Byte algorithm is one of the data compression algorithms. The Half Byte algorithm utilizes four left bits that are often the same sequentially especially text files. When the first four bits of the character are received in a row seven or more times, the algorithm compresses the data with marker bits, then the first character of the same four-bit series is followed by the last four-bit pair of the next series

show abstract

“…e authors in [26] used a neural network to find out the mapping relationship between plaintexts, ciphertexts, and the key in simplified DES (S-DES). e author in [27] developed a feedforward neural network that discovers the plaintext from the ciphertext without the key in the AES cipher. e authors in [28] attacked on the round-reduced Speck32/64 by using deep residual neural networks, where they trained the neural networks to distinguish the output of Speck with a given input difference based on the chosen plaintext attack.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Cryptanalysis of Lightweight Block Ciphers

2020

Security and Communication Networks

View full text Add to dashboard Cite

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.

show abstract

Research on Plaintext Restoration of AES Based on Neural Network

Cited by 19 publications

References 6 publications

Multiple Differential Distinguisher of SIMECK32/64 Based on Deep Learning

Multiple Differential Distinguisher of SIMECK32/64 Based on Deep Learning

File Compression Application Design Using Half Byte Algorithm

Deep Learning-Based Cryptanalysis of Lightweight Block Ciphers

Contact Info

Product

Resources

About