Recurrent neural networks (RNNs) represent the state of the art in translation, image captioning, and speech recognition. They are also capable of learning algorithmic tasks such as long addition, copying, and sorting from a set of training examples. We demonstrate that RNNs can learn decryption algorithms -- the mappings from plaintext to ciphertext -- for three polyalphabetic ciphers (Vigenere, Autokey, and Enigma). Most notably, we demonstrate that an RNN with a 3000-unit Long Short-Term Memory (LSTM) cell can learn the decryption function of the Enigma machine. We argue that our model learns efficient internal representations of these ciphers 1) by exploring activations of individual memory neurons and 2) by comparing memory usage across the three ciphers. To be clear, our work is not aimed at 'cracking' the Enigma cipher. However, we do show that our model can perform elementary cryptanalysis by running known-plaintext attacks on the Vigenere and Autokey ciphers. Our results indicate that RNNs can learn algorithmic representations of black box polyalphabetic ciphers and that these representations are useful for cryptanalysis.
Characterizing unknown ciphers is a central problem in cryptography. The comprehensive @cite_16 lists a wealth of methods, from frequency analysis to chosen-plaintext attacks. Papers such as @cite_4 offer additional methods. While these methods can be effective under the right conditions, they do not generalize well outside certain classes of ciphers. This has led researchers to propose several machine learning-based approaches. One work @cite_7 used genetic algorithms to recover the secret key of a simple substitution cipher. A review paper by @cite_0 proposes cipher classification with machine learning.
@cite_0 @cite_16 @cite_4 @cite_7
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Cryptanalysis is very important step for auditing and checking strength of any cryptosystem. Some of these cryptosystem ensures confidentiality and security of large information exchange from source to destination using symmetric key cryptography. The cryptanalyst investigates the strength and identifies the weakness of the key as well as enciphering algorithm. With the increase in key size, the time and effort required predicting the correct key increases. So, the Trend of increasing key size from 1 Byte to 8 Bytes to strengthen the cryptosystem and hence algorithm continues with compromise on the cost of time and computation. Automatic Variable Key (AVK) based symmetric key cryptosystem is an alternative to this style by fixing up key size and adding security level direction. Whenever any new cryptographic method is invented to replace existing vulnerable cryptographic method, it’s deep analysis from all perspectives (Hacker Cryptanalyst as well as User) is desirable and proper study and evaluation of its performance is must. New cryptic techniques may exploit benefits of advances in computational methods like ANN, GA, SI etc. These techniques for cryptanalysis are changing drastically to reduce cryptographic complexity. In this paper a detailed survey and direction of development work has been conducted. The work compares these new methods with state of art approaches and presents future scope and directions from the cryptic mining perspectives. CRYPTOGRAPHIC PROTOCOLS. Protocol Building Blocks. Basic Protocols. Intermediate Protocols. Advanced Protocols. Esoteric Protocols. CRYPTOGRAPHIC TECHNIQUES. Key Length. Key Management. Algorithm Types and Modes. Using Algorithms. CRYPTOGRAPHIC ALGORITHMS. Data Encryption Standard (DES). Other Block Ciphers. Other Stream Ciphers and Real Random-Sequence Generators. Public-Key Algorithms. Special Algorithms for Protocols. THE REAL WORLD. Example Implementations. Politics. SOURCE CODE.source Code. References. This paper considers a new approach to cryptanalysis based on the application of a directed random search algorithm called a genetic algorithm. It is shown that such a algorithm could be used to discover the key for a simple substitution cipher.