A Hybrid Genetic Algorithm and Hidden Markov Model-Based Hashing Technique for Robust Data Security

Abstract

The growing dependence on technology to store, process, and transmit data across interconnected systems has significantly elevated the need for robust data security. Modern computer systems emphasize the critical principles of authentication and data integrity. With the rise in cyber threats, the importance of securing data transactions against unauthorized or unintentional modifications has become more apparent than ever. As computers continue to play an increasingly vital role in daily operations, managing and safeguarding data alterations is essential. To address these challenges, businesses must adopt proactive measures to reinforce the security of sensitive data and passwords. Hashing functions, a well-established cryptographic approach, have proven effective in addressing a wide range of authentication and data integrity issues. A hash function generates a fixed-length output, known as a "digest," from an input. This one-way function is irreversible, providing a secure method of encoding data. However, hash functions are still vulnerable to various attacks, including dictionary attacks, brute-force attacks, and the use of lookup tables. The strength of a hashing function can be evaluated based on the number of attempts required to break it, the size of the hash key, and the specific algorithm employed. In response, this study proposes a novel hashing technique that integrates a Genetic Algorithm (GA) and Hidden Markov Models within a block hashing framework. Inspired by evolutionary biology, the GA applies operators such as mutation, crossover (recombination), and selection to simulate natural selection, offering a dynamic and efficient method for enhancing data security. The proposed algorithm utilizes the Hill cipher as an encryption mechanism and incorporates a singular Hill cipher key matrix to enhance security and reduce the likelihood of hash collisions or reversals. Experimental results demonstrate that the developed algorithm exhibits strong resistance to multiple attack types and outperforms several existing methods in terms of accuracy and robustness.

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