Modern Methods of Generating Pseudo Random Numbers: Advantages and Disadvantages

This article provides a comprehensive overview of contemporary approaches to generating pseudo-random numbers, examining their benefits and drawbacks across various fields such as cryptography, simulations, and computational modeling. It starts by defining pseudo-random numbers, contrasting them with true random numbers, and highlights their deterministic nature which results in reproducibility but introduces vulnerabilities in terms of security. The author discusses several specific methods, including Linear Congruential Generators (LCGs), the Mersenne Twister algorithm, Cryptographically Secure Pseudo-Random Number Generators (CSPRNGs), Xorshift, and quantum-based methods. Each method's advantages, such as efficiency, scalability, high-quality randomness, and cryptographic security, are balanced against disadvantages like predictability, periodicity, computational overhead, and resource intensiveness. Significant attention is given to the practical implications of each method, indicating the necessity of careful selection based on application requirements. Emphasis is placed on the importance of randomness quality for secure cryptographic applications, highlighting the risks associated with predictability in pseudo-random number generators. Finally, the article identifies future directions in the field, suggesting potential advancements through the integration of quantum computing and machine learning techniques, aimed at enhancing unpredictability and security in pseudo-random number generation. This forward-looking perspective underscores the evolving nature of computational methods and the continuous need for innovation in this critical area.