A Critical Reassessment of Evolutionary Algorithms on the cryptanalysis of the simplified data encryption standard algorithm
This work addresses a critical flaw in applying evolutionary algorithms to cryptanalysis, which is an incremental reassessment for researchers in cryptography and optimization.
The paper tackles the problem of using genetic algorithms for cryptanalysis of a simplified encryption standard, showing that the standard fitness function based on n-gram statistics is irrelevant due to a lack of correlation with the distance to the real key. As a result, the genetic algorithm performs worse than random search in this context.
In this paper we analyze the cryptanalysis of the simplified data encryption standard algorithm using meta-heuristics and in particular genetic algorithms. The classic fitness function when using such an algorithm is to compare n-gram statistics of a the decrypted message with those of the target message. We show that using such a function is irrelevant in case of Genetic Algorithm, simply because there is no correlation between the distance to the real key (the optimum) and the value of the fitness, in other words, there is no hidden gradient. In order to emphasize this assumption we experimentally show that a genetic algorithm perform worse than a random search on the cryptanalysis of the simplified data encryption standard algorithm.