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Showing 4 results for Cryptanalysis
Mahdi Ahmadipari, Meysam Moradi,
Volume 4, Issue 1 (9-2015)
Abstract
In recent years, the use of Meta-heuristic algorithms on various problem taken into consideration. Meta-heuristic algorithms in solving various problem, different performance show. An Meta-heuristic algorithm to solve a particular problem may have better performance than other algorithms and poorer performance have in other issue. In this study the performance of Meta-heuristic algorithms for a specific problem that explore cryption key Vigenere encryption algorithm will be examined. And Meta-heuristic different algorithms performance in terms of accuracy and speed of convergence of the results will be cryptanalyzed and the best algorithm is selected
Meysam Moradi, Mahdi Abbasi,
Volume 7, Issue 2 (3-2019)
Abstract
For many years, cryptanalysis has been considered as an attractive topic in jeopardizing the security and resistance of an encryption algorithm. The SDES encryption algorithm is a symmetric cryptography algorithm that performs a cryptographic operation using a crypt key. In the world of encryption, there are many search algorithms to cryptanalysis. In these researches, brute force attack algorithm has been used as a complete search algorithm, genetic algorithm as an evolutionary intelligence algorithm, and standard particle swarm as an optimization a swarm intelligence as algorithm. Along with these algorithms, a genetic algorithm has been also introduced by adjusting and designing the parameters and design algorithms has been introduced to discover of crypt key. There are attempts to evaluate the performance of different algorithms for cryptanalysis of the SDES encryption algorithm.
Iman Mirzaali Mazandarani, Dr Nasour Bagheri, Dr Sadegh Sadeghi,
Volume 12, Issue 1 (9-2023)
Abstract
With the increasing and widespread application of deep learning and neural networks across various scientific domains and the notable successes achieved, deep neural networks were employed for differential cryptanalysis in 2019. This marked the initiation of growing interest in this research domain. While most existing works primarily focus on enhancing and deploying neural distinguishers, limited studies have delved into the intrinsic principles and learned characteristics of these neural distinguishers. In this study, our focus will be on analyzing block ciphers such as Speck, Simon, and Simeck using deep learning. We will explore and compare the factors and components that contribute to better performance. Additionally, by detailing attacks and comparing results, we aim to address the question of whether neural networks and deep learning can effectively serve as tools for block cipher cryptanalysis or not.
Dr. Marzieh Vahid Dastjerdi, Mr. Majid Rahimi,
Volume 14, Issue 1 (9-2025)
Abstract
The objective of this paper is to analyze and evaluate the behaviour of modular addition and subtraction in symmetric cipher attacks. Modular addition is one of the most widely used nonlinear operators in symmetric cryptographic algorithms. In ARX symmetric algorithms, only three operators are utilized: modular addition, rotation, and XOR. In ARX-like algorithms, modular subtraction or a substitution box is employed, in addition to the standard ARX operations. Since modular subtraction exhibits similar behaviour to modular addition, its behaviour against cryptanalytic attacks has not been explicitly studied in the literature. Therefore, this paper aims to provide a comprehensive overview of the behaviour of modular addition and subtraction in differential, linear, integral cryptanalysis based on division property, and rotational attacks, using both manual analysis and automated methods via MILP (Mixed-Integer Linear Programming). We demonstrate that there is no difference between modular addition and subtraction in differential, linear, and rotational cryptanalysis. However, in integral cryptanalysis based on the division property, these two operations behave differently.
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