Binary cat swarm optimization for cryptanalysis
Seeven Amic, K. M. S. Soyjaudah, Gianeshwar Ramsawock · 2017
This research paper investigates the relevance of the Cat Swarm Optimization (CSO) algorithm to cryptanalysis. It is a relatively new evolutionary metaheuristic technique to solve those problems which belong to NP-hard class based on the swarm intelligence of felids, commonly known as cats. Cryptanalysis is an active field of experimental research as it can consolidate or depreciate the viability of modern ciphers. To the extent of our knowledge, there is no evidence that the CSO has ever been applied to the cryptanalysis problem. Experimental outcomes demonstrate that the Binary Cat Swarm Optimization (BCSO) algorithm is efficient for the cryptanalysis of the Data Encryption Standard (DES) using chosen plaintext attack. Further, it produces optimal keys with higher fitness as compared to Particle Swarm Optimization. This paper shows that BCSO can be successfully utilized for the cryptanalysis of block ciphers with promising outcomes.