Modification of Some Solution Techniques of Combinatorial Optimization Problems to Analyze the Transposition Cipher

In this paper we attempt to use a new direction in cryptanalysis of classical crypto systems. The new direction represented by considering some of classical crypto systems, like transposition cipher problem (TCP), as a combinatorial optimization problem (COP), then using the known solving methods of COP, with some modification, to cryptanalysis the TCP. In this work we investigate to use Branch and Bound (BAB) and one of swarm algorithms as a local search method. The main aim of the research presented in this paper is to investigate the use of some optimization methods in the fields of cryptanalysis and cryptographic function generation. These techniques were found to provide a successful method of automated cryptanalysis of a variety of the classical ciphers. Keywords: cryptography, cryptanalysis, Classical Ciphers, Transposition Cipher, Branch and Bound, Swarm Intelligence, Bees Algorithm

A Review on Biological Inspired Computation in Cryptology

Cryptology is a field that concerned with cryptography and cryptanalysis. Cryptography, which is a key technology in providing a secure transmission of information, is a study of designing strong cryptographic algorithms, while cryptanalysis is a study of breaking the cipher. Recently biological approaches provide inspiration in solving problems from various fields. This paper reviews major works in the application of biological inspired computational (BIC) paradigm in cryptology. The paper focuses on three BIC approaches, namely, genetic algorithm (GA), artificial neural network (ANN) and artificial immune system (AIS). The findings show that the research on applications of biological approaches in cryptology is minimal as compared to other fields. To date only ANN and GA have been used in cryptanalysis and design of cryptographic primitives and protocols. Based on similarities that AIS has with ANN and GA, this paper provides insights for potential application of AIS in cryptology for further research

How to distinguish between a block cipher and a random permutation by lowering the input entropy

IEEE 35th International Carnahan Conference on Security Technology. Londres, 16-19 October 2001A novel cryptanalysis technique is presented, and its suitability for distinguishing a block cipher algorithm or a hash function from a random permutation is explained. Additionally, we propose a genetic algorithm based implementation and show some preliminary results of these ideas on reduced rounds versions of the block cipher TEA

A Binomial Crossover Based Artificial Bee Colony Algorithm for Cryptanalysis of Polyalphabetic Cipher

Cryptography is one of the common approaches to secure private data and cryptanalysis involves breaking down a coded cipher text without having the key. Cryptanalysis by brute force cannot be accepted as an effective approach and hence, metaheuristic algorithms performing systematic search can be applied to derive the optimal key. In this study, our aim is to examine the overall suitability of Artificial Bee Colony algorithm in the cryptanalysis of polyalphabetic cipher. For this purpose, using a number of different key lengths in both English and Turkish languages, basic Artificial Bee Colony algorithm (ABC) is applied in the cryptanalysis of Vigenere cipher. In order to improve the ABC algorithm\u27s convergence speed, a modified binomial crossover based Artificial Bee Colony algorithm (BCABC) is proposed by introducing a binomial crossoverbased phase after employed bee phase for a precise search of global optimal solution. Different keys in various sizes, various cipher texts in both English and Turkish languages are used in the experiments. It is shown that optimal cryptanalysis keys produced by BCABC are notably competitive and better than those produced by basic ABC for Vigenere cipher analysis

A survey on machine learning applied to symmetric cryptanalysis

In this work we give a short review of the recent progresses of machine learning techniques applied to cryptanalysis of symmetric ciphers, with particular focus on artificial neural networks. We start with some terminology and basics of neural networks, to then classify the recent works in two categories: "black-box cryptanalysis", techniques that not require previous information about the cipher, and "neuro-aided cryptanalysis", techniques used to improve existing methods in cryptanalysis

Using quantum key distribution for cryptographic purposes: a survey

The appealing feature of quantum key distribution (QKD), from a cryptographic viewpoint, is the ability to prove the information-theoretic security (ITS) of the established keys. As a key establishment primitive, QKD however does not provide a standalone security service in its own: the secret keys established by QKD are in general then used by a subsequent cryptographic applications for which the requirements, the context of use and the security properties can vary. It is therefore important, in the perspective of integrating QKD in security infrastructures, to analyze how QKD can be combined with other cryptographic primitives. The purpose of this survey article, which is mostly centered on European research results, is to contribute to such an analysis. We first review and compare the properties of the existing key establishment techniques, QKD being one of them. We then study more specifically two generic scenarios related to the practical use of QKD in cryptographic infrastructures: 1) using QKD as a key renewal technique for a symmetric cipher over a point-to-point link; 2) using QKD in a network containing many users with the objective of offering any-to-any key establishment service. We discuss the constraints as well as the potential interest of using QKD in these contexts. We finally give an overview of challenges relative to the development of QKD technology that also constitute potential avenues for cryptographic research.Comment: Revised version of the SECOQC White Paper. Published in the special issue on QKD of TCS, Theoretical Computer Science (2014), pp. 62-8