Genetic algorithms (GAs) are a class of optimization algorithms. GAs attempt to solve problems through modeling a simplified version of genetic processes. There are many problems for which a GA approach is useful. It is, however, undetermined if cryptanalysis is such a problem. Therefore, this work explores the use of GAs in cryptography. Both traditional crypt analysis and GA-based methods are implemented in software. The results are then com pared using the metrics of elapsed time and percentage of successful decryptions. A de termination is made for each cipher under consideration as to the validity of the GA-based approaches found in the literature. In general, these GA-based approaches are typical of the field. Of the genetic algorithm attacks found in the literature, totaling twelve, seven were re-implemented. Of these seven, only three achieved any success. The successful attacks were those on the transposition and permutation ciphers by Matthews [20], Clark [4], and Griindlingh and Van Vuuren [13], respectively. These attacks were further investigated in an attempt to improve or extend their success. Unfortunately, this attempt was unsuccessful, as was the attempt to apply the Clark [4] attack to the monoalphabetic substitution cipher and achieve the same or indeed any level of success. Overall, the standard fitness equation genetic algorithm approach, and the scoreboard variant thereof, are not worth the extra effort involved. Traditional cryptanalysis methods are more successful, and easier to implement. While a traditional method takes more time, a faster unsuccessful attack is worthless. The failure of the genetic algorithm approach indicates that supplementary research into traditional cryptanalysis methods may be more useful and valuable than additional modification ofGA-based approaches.

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Genetic algorithms in cryptography

In this paper, we present a new approach for cryptanalysis of four-round DES based on genetic algorithm. An efficient fitness measure is used to find some optimum keys with higher fitness values. Some valuable bits in these optimum keys which generate apparent deviation from the other observed bits will turn up. Then, these valuable bits can be applied to find other bits. The results indicate that genetic algorithm is successful at breaking the four-round DES cipher, but perhaps more significantly, this is a promising method applied to break other Feistel block cipher.

Cryptanalysis of Four-Round DES Based on Genetic Algorithm

In this paper, we propose an approach for identification of encryption method for block ciphers using support vector machines. The task of identification of encryption method from cipher text only is considered as a document categorization task. We address the issues in representing a cipher text by a document vector. We consider the common dictionary based method and the class specific dictionary based method for generating a document vector from a cipher text. As the dimension of document vector is large, support vector machines based classifiers are considered for identification of encryption method. We present the performance of the proposed approach for cipher texts generated using five block ciphers.

Identification of Block Ciphers using Support Vector Machines

Cryptanalysis attempts identify the weaknesses in the algorithms used to encrypt code or the methods used to generate keys. In this study, we use pattern recognition techniques for identification of encryption algorithms for block ciphers. The following block cipher algorithms, DES, IDEA, AES, and RC operating in ECB mode were considered. Eight different classification techniques which are: Naive Bayesian (NB), Support Vector Machine (SVM), neural network (MPL), Instance based learning (IBL), Bagging (Ba), AdaBoostM1, Rotaion Forest (RoFo), Decision Tree (C4.5) were used to identify the cipher text. This study aims to find the best classification algorithm to identify the cipher encryption method. The performance of each of the classifiers was presented, and the simulation results show that, in general, the RoFo classifier has the highest classification accuracy.

Classifying encryption algorithms using pattern recognition techniques

With the exponential growth of networked system and its applications such as e-commerce, the demand for effective internet security is increasing. Cryptology is the science and study of systems for secret communication. It consists of two complementary fields of study: cryptography and cryptanalysis. In this paper the cryptanalysis of a poly alphabetic substitution cipher (Vigenere cipher) by applying genetic algorithms is presented. The applicability of genetic algorithms for searching the key space of encryption scheme is studied. The frequency analysis is used as an essential factor in the objective function.

A cryptanalytic attack on Vigenère cipher using genetic algorithm

Cryptology is the science and study of systems for secret communications. It consists of two complementary fields of study: cryptography and cryptanalysis. In this paper, we propose a cryptanalysis method based on genetic algorithms to break the Vernam cipher. The proposed approach is a ciphertext-only attack in which we don't know any plaintext; the only thing we have to know is that the plaintext is an English document. Let M=m/sub 1/, m/sub 2/, ... denote a plaintext bit stream and K=k/sub 1/, k/sub 2/, ... a key bit stream. The Vernam cipher generates a ciphertext bit stream C=E/sub k/(M)=c/sub 1/, c/sub 2/, ..., where c/sub i/=(m/sub i/+k/sub i/) mod p, p is a base. In our work, we first tried to find out the key stream K=k/sub 1/, k/sub 2/, ... from an intercepted ciphertext C by genetic algorithms and then use them to break the cipher.

Cheng-Yan Kao

Papers

A genetic algorithm for ciphertext-only attack in cryptanalysis