A Genetic Algorithm-Based Approach for Enhancing Data Security

Data security has become one of the most critical concerns in modern information technology due to the rapid growth of digital communication, cloud computing, Internet of Things (IoT) devices, online financial transactions, and large-scale data sharing. The increasing frequency of cyberattacks, data breaches, and unauthorized access has created an urgent need for stronger and more intelligent security mechanisms capable of protecting sensitive information. Although conventional encryption algorithms such as the Advanced Encryption Standard (AES) and Rivest–Shamir–Adleman (RSA) provide robust protection, the overall security of these systems largely depends on the quality, randomness, and unpredictability of the cryptographic keys employed. This study presents a simple framework that utilizes a Genetic Algorithm (GA) to improve encryption key generation through evolutionary optimization. The proposed approach represents candidate encryption keys as chromosomes and applies the fundamental GA operations of population initialization, fitness evaluation, tournament selection, crossover, and mutation to iteratively generate stronger keys with improved randomness. The effectiveness of the proposed framework is evaluated using simulated experimental data based on four performance indicators: fitness value, key entropy, randomness score, and encryption execution time.