A Critical Reassessment of Evolutionary Algorithms on the cryptanalysis of the simplified data encryption standard algorithm

TL;DR

This paper critically evaluates the effectiveness of evolutionary algorithms, specifically genetic algorithms, in cryptanalyzing the simplified DES, revealing their limitations and showing they perform worse than random search due to lack of fitness gradient.

Contribution

It demonstrates that standard fitness functions are ineffective for genetic algorithms in this context and provides experimental evidence of their poor performance compared to random search.

Findings

Genetic algorithms perform worse than random search in this cryptanalysis task.

Standard n-gram based fitness functions are irrelevant for genetic algorithms in this context.

No correlation exists between fitness value and proximity to the correct key.

Abstract

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.