Cryptanalysis of Classic Ciphers Using Hidden Markov Models

Cryptanalysis is the study of identifying weaknesses in the implementation of cryptographic algorithms. This process would improve the complexity of such algo- rithms, making the system secure. In this research, we apply Hidden Markov Models (HMMs) to classic cryptanaly- sis problems. We show that with sufficient ciphertext, an HMM can be used to break a simple substitution cipher. We also show that when limited ciphertext is avail- able, using multiple random restarts for the HMM increases our chance of successful decryption

Reified Context Models

A classic tension exists between exact inference in a simple model and approximate inference in a complex model. The latter offers expressivity and thus accuracy, but the former provides coverage of the space, an important property for confidence estimation and learning with indirect supervision. In this work, we introduce a new approach, reified context models, to reconcile this tension. Specifically, we let the amount of context (the arity of the factors in a graphical model) be chosen "at run-time" by reifying it---that is, letting this choice itself be a random variable inside the model. Empirically, we show that our approach obtains expressivity and coverage on three natural language tasks

Cryptanalysis of Homophonic Substitution Cipher Using Hidden Markov Models

We investigate the effectiveness of a Hidden Markov Model (HMM) with random restarts as a mean of breaking a homophonic substitution cipher. Based on extensive experiments, we find that such an HMM-based attack outperforms a previously de- veloped nested hill climb approach, particularly when the ciphertext message is short. We then consider a combination cipher, consisting of a homophonic substitution and a column transposition. We develop and analyze an attack on such a cipher. This attack employs an HMM (with random restarts), together with a hill climb to recover the column permutation. We show that this attack can succeed on relatively short ci- phertext messages. Finally, we test this combined attack on the unsolved Zodiac 340 cipher

Cryptanalysis of Homophonic Substitution-Transposition Cipher

Homophonic substitution ciphers employ a one-to-many key to encrypt plaintext. This is in contrast to a simple substitution cipher where a one-to-one mapping is used. The advantage of a homophonic substitution cipher is that it makes frequency analysis more difficult, due to a more even distribution of plaintext statistics. Classic transposition ciphers apply diffusion to the ciphertext by swapping the order of letters. Combined transposition-substitution ciphers can be more challenging to cryptanalyze than either cipher type separately. In this research, we propose a technique to break a combined simple substitution- column transposition cipher. We also consider the related problem of breaking a combination homophonic substitution-column transposition cipher. These attacks extend previous work on substitution ciphers. We thoroughly analyze our attacks and we apply the homophonic substitution-columnar transposition attack to the unsolved Zodiac-340 cipher

Cryptanalysis of the Purple Cipher using Random Restarts

Cryptanalysis is the process of trying to analyze ciphers, cipher text, and crypto systems, which may exploit any loopholes or weaknesses in the systems, leading us to an understanding of the key used to encrypt the data. This project uses Expectation Maximization (EM) approach using numerous restarts to attack decipherment problems such as the Purple Cipher. In this research, we perform cryptanalysis of the Purple cipher using genetic algorithms and hidden Markov models (HMM). If the Purple cipher has a fixed plugboard, we show that genetic algorithms are successful in retrieving the plaintext from cipher text with high accuracy. On the other hand, if the cipher has a plugboard that is not fixed, we can decrypt the cipher text with increasing accuracy given an increase in population size and restarts. We performed the cryptanalysis of PseudoPurple, which is less complex but more powerful than Purple using HMMs. Though we could not decrypt cipher text produced by PseudoPurple with good accuracy, there is an increase in accuracy of the decrypted plaintext with an increase in the number of restarts

Generative Adversarial Networks for Classic Cryptanalysis

The necessity of protecting critical information has been understood for millennia. Although classic ciphers have inherent weaknesses in comparison to modern ciphers, many classic ciphers are extremely challenging to break in practice. Machine learning techniques, such as hidden Markov models (HMM), have recently been applied with success to various classic cryptanalysis problems. In this research, we consider the effectiveness of the deep learning technique CipherGAN---which is based on the well- established generative adversarial network (GAN) architecture---for classic cipher cryptanalysis. We experiment extensively with CipherGAN on a number of classic ciphers, and we compare our results to those obtained using HMMs