Proceedings ArticleDOI
Improved cryptanalysis combining differential and artificial neural network schemes
Moises Danziger,Marco Aurelio Amaral Henriques +1 more
- pp 1-5
TLDR
The results show that, even with a small amount of samples, the neural network was able to map the relation between inputs, keys and outputs and to obtain the correct values for the key bits k0, k1 and k4.Abstract:
In this work we show the application of a neural cryptanalysis approach to S-DES input-output-key data to test if it is capable of mapping the relations among these elements. The results show that, even with a small amount of samples (about 0,8% of all data), the neural network was able to map the relation between inputs, keys and outputs and to obtain the correct values for the key bits k 0 , k 1 and k 4 . By applying differential cryptanalysis techniques on the key space, it was possible to show that there is an explanation about the neural network partial success with some key bits. After implementing new s-boxes, which are more resistant to the differential attack, the neural network was not able to point out bits of the key any more. We believe that this new methodology of attack and repair assessment using neural networks has the potential to contribute in the future analysis of other cryptographic algorithms.read more
Citations
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Book ChapterDOI
Improving Attacks on Round-Reduced Speck32/64 Using Deep Learning
TL;DR: A highly selective key search policy based on a variant of Bayesian optimization which, together with the neural distinguishers, can be used to reduce the remaining security of 11-round Speck32/64 to roughly 38 bits.
Journal ArticleDOI
Deep Learning-Based Cryptanalysis of Lightweight Block Ciphers
TL;DR: The proposed generic cryptanalysis model based on deep learning (DL), where the model tries to find the key of block ciphers from known plaintext-ciphertext pairs, shows the feasibility and indicates that the DL technology can be a useful tool for the cryptanalysis of blockciphers when the keyspace is restricted.
Proceedings Article
Neural-Cryptanalysis of Classical Ciphers
TL;DR: Artificial neural networks are applied to automatically “assist” cryptanalysts into exploiting cipher weaknesses and provide the first ciphertext-only attack on substitution ciphers based on neural networks.
Journal ArticleDOI
Research on Plaintext Restoration of AES Based on Neural Network
Xinyi Hu,Yaqun Zhao +1 more
TL;DR: Backpropagation neural networks are used to perform cryptanalysis on AES in an attempt to restore plaintext, and the results show that the neural network can restore the entire byte with a probability of more than 40, and restore more than half of the plaintext bytes above 89%.
Book ChapterDOI
Performance Comparison Between Deep Learning-Based and Conventional Cryptographic Distinguishers
Emanuele Bellini,Matteo Rossi +1 more
TL;DR: This work proposes two Deep Learning (DL) based distinguishers against the Tiny Encryption Algorithm (TEA) and its evolution RAIDEN, and shows how these two distinguishers outperform a conventional statistical distinguisher, with no prior information on the cipher.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Book
Differential Cryptanalysis of the Data Encryption Standard
Eli Biham,Adi Shamir +1 more
TL;DR: This book introduces a new cryptographic method, called differential cryptanalysis, which can be applied to analyze cryptosystems, and describes the cryptanalysis of DES, deals with the influence of its building blocks on security, and analyzes modified variants.
Book
Cryptographic Boolean Functions and Applications
TL;DR: This book serves as a complete resource for the successful design or implementation of cryptographic algorithms or protocols using Boolean functions; provides engineers and scientists with a needed reference for the use of Boolean functions in cryptography; and addresses the issues of cryptographic Boolean functions theory and applications in one concentrated resource.
Journal ArticleDOI
The structured design of cryptographically good s-boxes
TL;DR: The procedure is proven to construct s-boxes which are bijective, are highly nonlinear, possess the strict avalanche criterion, and have output bits which act (vitually) independently when any single input bit is complemented.
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