Amino Acid Encryption Method Using Genetic Algorithm for Key Generation

Abstract

In this new information era, the transfer of data and information has become a very important matter. Transferred data must be kept secured from unauthorized persons using cryptography. The science of cryptography depends not only on complex mathematical models but also on encryption keys. Amino acid encryption is a promising model for data security. In this paper, we propose an amino acid encryption model with two encryption keys. The first key is generated randomly using the genetic algorithm. The second key is called the protein key which is generated from converting DNA to a protein message. Then, the protein message and the first key are used in the modified Playfair matrix to generate the cypher message. The experimental results show that the proposed model survives against known attacks such as the Brute-force attack and the Ciphertext-only attack. In addition, the proposed model has been tested over different types of characters including white spaces and special characters, as all the data is encoded to 8-bit binary. The performance of the proposed model is compared with other models using encryption time and decryption time. The model also balances all three principles in the CIA triad.

A multi-level random key cryptosystem based on DNA encoding and state-changing mealy machine

“…Like Sayantani et al [11] they also adopted DNA Transcription and Translation into algorithms to convert the plaintext into DNA bases although their method stayed more true to the biological operations that occur and the components that are used in those operations. Sakr et al [21] used a novel approach for encryption. Their work used genetic algorithm to generate a random security key where randomness was measured using run test of randomness [10].…”

Design of nonlinear control system for motion trajectory of industrial handling robot

“…The experimental results showed that the intelligent system of power grid could resist various intrusion behaviors of unknown data sources under the optimization of the algorithm. 11 Song et al combined a MO-GA and a discrete phase model to improve the separator. The experimental results showed that the pressure drop and separation efficiency of the separator were significantly improved under the optimal design of the algorithm.…”

“…According to Equation (10), smaller reciprocal means worse fitness value, and lower probability of the risk species for the individual, thus the offspring optimization can be performed. In the crossover calculation of the algorithm, the real numbers of two good parents of the same selection at the j point are crossed to form a new individual, and the crossover operation is expressed as Equation (11).…”

“…In Equation (11), 𝜙 denotes an individual and h is a random number between the interval 0 and 1. Finally, in order to increase the diversity of the population, the individuals are subjected to a mutation operation denoted by Equation (12).…”

“…Sakr et al applied genetic algorithms to the field of cryptography by employing genetic algorithms for random generation in an amino acid cryptographic model. The experimental results showed that the intelligent system of power grid could resist various intrusion behaviors of unknown data sources under the optimization of the algorithm 11 . Song et al combined a MO‐GA and a discrete phase model to improve the separator.…”