11/25/2023 0 Comments A pseudorandom sequence meaning![]() ![]() PRNG algorithms, such as Mersenne Twister (MT) and Xorshift, and cryptographic PRNGs are deterministic, producing the same sequence of numbers with a unique period when the same initial values, called “seeds”, are input.īecause many PRNG algorithms are open and publicly available, applying PRNGs to online applications and services requires extensive care to hide the seeds and implementation code, thus preventing malicious users from predicting the next number to be generated. A PRNG is validated through a statistical test suite, such as NIST SP 800-22rev1a (NIST test suite), which is commonly used to evaluate the robustness and fairness of the generated random numbers and consists of 15 statistical tests. PRNGs are algorithms that rapidly generate uncorrelated and random sequences of numbers that appear to be sufficiently complex and random for ordinary purposes. PRNGs have been developed as general-purpose software modules for many years and have been widely adopted in domains where a vast array of random numbers are required in information systems, such as the security field. 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. Pseudo-random number generators (PRNGs) are software algorithms generating a sequence of numbers approximating the properties of random numbers. ![]()
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