A cryptanalytic attack on Knapsack cipher using Differential Evolution algorithm

Sinha, Saptarshi Neil; Palit, Supravo; Molla, Mostafiz Amin; Khanra, Atreyee; Kule, Malay

doi:10.1109/raics.2011.6069326

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…Te trials validated cuckoo search as the optimal technique for automating the cryptographic analysis of a minimal cryptosystem. Diferential evolution was employed by Sinha et al [72] to solve the MHKC, and the results were compared to those obtained by a genetic algorithm (GA) to prove its efcacy. A genetic algorithm for the MHKC has been presented by Kochladze et al [73].…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Optimization and Comprehensive Analysis of Binary Nutcracker Optimization Algorithm: A Case Study of Feature Selection and Merkle–Hellman Knapsack Cryptosystem

et al. 2023

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In this paper, a binary variant of a novel nature-inspired metaheuristic algorithm called the nutcracker optimization algorithm (NOA) is presented for binary optimization problems. Because of the continuous nature of the classical NOA and the discrete nature of the binary problems, two different families of transfer functions, namely S-shaped and V-shaped, are extensively investigated for converting the classical NOA into a binary variant, namely BNOA, applicable for various binary problems. Additionally, BNOA is improved using a local search strategy based on effectively integrating some genetic operators into the BNOA’s exploitation and exploration; this additional variant is called BINOA. Both BNOA and BINOA are evaluated using three common binary optimization problems, including feature selection, 0-1 knapsack, and the Merkle–Hellman knapsack cryptosystem (MHKC), and are compared to several robust binary metaheuristic optimizers in terms of statistical information, statistical tests, and convergence speed. The experiential findings show that BINOA is better than the classical BNOA and the other rival optimizers for both the 0-1 knapsack problem and attacking MHKC and is on par with some algorithms, like the genetic algorithm for feature selection.

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Section: Literature Reviewmentioning

confidence: 99%

Performance Optimization and Comprehensive Analysis of Binary Nutcracker Optimization Algorithm: A Case Study of Feature Selection and Merkle–Hellman Knapsack Cryptosystem

et al. 2023

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“…Abdul-Halim et al [4] and Jain and Chaudhari [5] use a binary particle swarm optimization to attack Knapsack cryptosystem, they found that binary PSO is more efficient than the GA used by Spillman [2], and Garg et al [3]. Furthermore, Sinha [6] proves that differential evolution is a much efficient technique than genetic algorithm for attacking the knapsack cipher. a cryptanalytic attack on the knapsack cryptosystem using binary variant of firefly algorithm is done by Palit [7] showing that the performance of firefly algorithm is much better than GA in this purpose.…”

Section: Introductionmentioning

confidence: 99%

Cryptanalysis of Merkle-Hellman cipher using ant colony optimization

Grari

Lamzabi

Azouaoui

et al. 2021

IJ-AI

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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).

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