Random number generators, cryptology, encryption, linear congruential generator (LCG)

Random numbers have been used from past to present in many areas, both in daily life and for scientific, technical, and entertainment purposes. In addition to fields such as the gaming and entertainment industry, simulation and modeling, statistics and data science, machine learning and artificial intelligence, art and creative applications, and testing and software development, it has also become a fundamental tool needed in most cryptology applications. In the field of cryptology, random numbers are heavily relied upon in sensitive processes such as key generation and distribution, initialization vector determination, and authentication protocols. The more random and unpredictable these numbers are, the more secure the system becomes. Hence, the primary goal of developed cryptographic systems is to generate random numbers that are unpredictable, non-reproducible, and exhibiting strong statistical characteristics. This study aims to test whether the encryption process—performed by encrypting audio, image, and video files with software developed using random numbers and subjecting them to statistical testing—is successful. In the encryption process, a Pseudo-Random Number Generator (PRNG) algorithm based on the Linear Congruential Generator (LCG) is used to generate random numbers. The LCG is an efficient algorithm that is widely used in computers for pseudo-random number generation. This method generates each new number based on the previous one using specific mathematical parameters. In the conducted studies, encryption has been performed separately for audio, image, and video data. In image and video encryption, key-based permutation operations and S-Box-based advanced scrambling steps are also incorporated to improve data security. Thanks to this holistic approach, a robust and flexible encryption framework has been established for different types of data.