<p class="Abstract"><span id="docs-internal-guid-d3fe8e21-7fff-17fc-df0e-00893428243c"><span>The Merkle-Hellman (MH) cryptosystem is one of the earliest public key cryptosystems, which is introduced by Ralph Merkle and Martin Hellman in 1978 based on an NP-hard problem, known as the subset-sum problem. Furthermore, ant colony optimization (ACO) is one of the most nature-inspired meta-heuristic optimization, which simulates the social behaviour of ant colonies. ACO has demonstrated excellent performance in solving a wide variety of complex problems. In this paper, we present a novel ant colony optimization (ACO) based attack for cryptanalysis of MH cipher algorithm, where two different search techniques are used. Moreover, experimental study is included, showing the effectiveness of the proposed attacking scheme. The results show that ACO based attack is more suitable than many other algorithms like genetic algorithm (GA) and particle swarm optimization (PSO).</span></span></p>

Ant colony Optimization is a nature-inspired meta-heuristic optimization algorithm that gained a great interest in resolution of combinatorial and numerical optimization problems in many science and engineering domains. The aim of this work was to investigate the use of Ant Colony Optimization in cryptanalysis of Simplified Advanced Encryption Standard (S-AES), using a known plaintext attack. We have defined the essential components of our algorithm such as heuristic value, fitness function and the strategy to update pheromone trails. It is shown from the experimental results that our proposed algorithm allow us to break S-AES cryptosystem after exploring a minimum search space when compared with others techniques and requiring only two plaintext-ciphertext pairs.

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Recently, Deep Neural Networks have shown great deal of reliability and applicability as its applications spread in different areas. This paper proposes a cryptanalysis model based on Deep Neural Network, the neural network takes in plaintexts and their corresponding ciphertexts to predict the secret key of the cipher. We proposes two different approaches, in the first we use multi-layer perceptron (MLP). While in the second, the cryptanalysis problem is modeled as a multi-label classification problem, we introduce appropriate Deep Neural Network based methods for tackling such problem. We illustrate the effectiveness of the approach of the DNN-based cryptanalysis by attacking on Simplified AES block cipher. Therefore, specific metrics are readapted to the cryptanalysis context and used to evaluate the proposed schemes. The results indicate that treating cryptanalysis problem as multi-label classification is more suitable and can be a useful and promising tool for the cryptanalysis task.