Tests of pseudo-random number generator (PRNG) performance use deterministic analysis to expose weaknesses, which new PRNGs are designed to satisfy. Modern supervised learning algorithms offer an improved method to test PRNG performance. Richard Freytag offers a short, concrete, and intuitive exploration of how to apply machine learning as a black box in pseudo-random number generators.
Richard Freytag is the owner of a small development and contracting company, Freytag & Company, whose clients have included the Department of Defense. Most of Freytag & Company’s products target the .NET platform and range from email add-ins, phone apps, and desktop applications to SaaS offerings in the cloud, and its most important products combine machine learning and computer security as core features.
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