![]() ![]() The major disadvantage of TRNGs is the long time required to generate many random numbers compared with PRNGs, which is due to their dependence on physical phenomena and the need for specific hardware. Such TRNGs are often utilized in high-risk domains where genuine unpredictability is required, including security and finance. Many researchers have developed well-known TRNG implementations, starting with low speed rates up to 300 Gb/s random bit generation (RBG), and high speed rates up to 2 Tb/s RBG, and the fastest one of 250 Tb/s RBG was developed by Kim et al. TRNGs adopt physical phenomena with randomnesses, such as temporal properties of operating system user processes, thermal noise, shot noise, electronics noise, and the emission timing of radioactive decay, to generate random numbers. There are two common tools used to generate random numbers: true random number generators (TRNGs) utilizing physical phenomena and pseudo-random number generators (PRNGs) implemented as software algorithms. Random numbers are a fundamental tool for implementing fairness and an essential software component for implementation. For example, cryptography, gaming, machine learning, and a wide range of simulations such as molecular simulation and phase field simulation have utilized random numbers to implement unpredictable and nonarbitrary behaviors. With the increasingly unpredictable and nonarbitrary behaviors required by information systems in various fields, random numbers have played a crucial role in implementing unpredictable and dynamic behaviors. The experimental results also show that overfitting was observed after about 450,000 trials of learning, suggesting that there is an upper limit to the number of learning counts for a fixed-size neural network, even when learning with unlimited data. Such tailor-made PRNGs will effectively enhance the unpredictability and nonarbitrariness of a wide range of information systems, even if the seed numbers can be revealed by reverse engineering. This study opens the way for the “democratization” of PRNGs through the end-to-end learning of conventional PRNGs, which means that PRNGs can be generated without deep mathematical know-how. The experimental results show that our LPRNG successfully converted the sequence of seed numbers to random numbers that fully satisfy the NIST test suite. We conduct experimental studies to evaluate our learned pseudo-random number generator (LPRNG) by adopting cosine-function-based numbers with poor random number properties according to the NIST test suite as seed numbers. We remove the dropout layers from the conventional WGAN network to learn random numbers distributed in the entire feature space because the nearly infinite amount of data can suppress the overfitting problems that occur without dropout layers. In this approach, the existing Mersenne Twister (MT) PRNG is learned without implementing any mathematical programming code. ![]() In this paper, we propose a Wasserstein distance-based generative adversarial network (WGAN) approach to generating PRNGs that fully satisfy the NIST test suite. A PRNG is commonly validated through a statistical test suite, such as NIST SP 800-22rev1a (NIST test suite), to evaluate its robustness and the randomness of the numbers. They are critical components in many information systems that require unpredictable and nonarbitrary behaviors, such as parameter configuration in machine learning, gaming, cryptography, and simulation. “Random number generators: good ones are hard to find.” Communications of the ACM 31.10 (1988): 1192–1201.Pseudo-random number generators (PRNGs) are software algorithms generating a sequence of numbers approximating the properties of random numbers. The Royal Society, 1985.īarnsley, Michael F. “Iterated function systems and the global construction of fractals.” Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. 36–38.īernard Widynski, “Middle Square Weyl Sequence RNG”,. Germond, eds., Monte Carlo Method, National Bureau of Standards Applied Mathematics Series, vol. John von Neumann, “Various techniques used in connection with random digits,” in A.S. The drunkard’s walk: How randomness rules our lives. A college course on relativity and cosmology. “Monte carlo method.” NBS Applied Mathematics Series 12 (1951).Ĭheng, Ta-Pei, and Brian H. ![]() “Dynamical bias in the coin toss.” SIAM review 49.2 (2007): 211–235.įorsythe, G. “What is a random sequence?.” The American mathematical monthly 109.1 (2002): 46–63.ĭiaconis, Persi, Susan Holmes, and Richard Montgomery. Augustine of Hippo, Confessiones lib xi, cap xiv, sec 17, circa 400 AD. ![]()
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