Evaluation of cryptographic key generation performance using evolutionary algorithm

The process of key generation is at the heart of each cryptography method’s encryption and decryption. As a result, it is necessary to make the key as strong as possible in order to eliminate the risk of hacking, resulting in extremely secure data. The process of key detection will be difficult if all key generating methods are defined with a high level of randomness. Author has proposed an evolutionary method of genetic algorithm to improve key generation performance. It also includes a frame with a population gene size of 16 bytes. After generating the population size ‘M', the fitness function is used to choose the best genes. The selected genes are then subjected to mutation and crossover functions. The best genes are then added to the population based on the results of the operations. Finally, we generate a key from the population set by selecting a gene at random. The technique described above is repeated N times. The sole gene is usually measured from a 16-byte key. If a 32-byte key is required, technique chooses two genes at random and merge them to generate the appropriate key. Method compares the performance of key creation with two built-in evolutionary methods using Python. In addition to the time complexity many other parameters like entropy values, and size of ciphertexthave been considered for evaluation in this proposed work. This proposed system depicts that the Evolutionary key generation (EKG) approach is nearly as good as the Password key generation technique, and that EKG is significant than the Random key generation technique.