![]() And we also deep dive into the trained model to show how it worked and extract useful information from it. This blog aims to show how to train a machine learning model that can reach 100% accuracy in generating random numbers without knowing the seed. Also, we have achieved a higher accuracy. We simplified the structure of the neural network model from the one proposed in that post. We started by breaking a simple PRNG, namely XORShift, following the lead of the post published in. By cracking here, we mean that we can predict the sequence of the random numbers using previously generated numbers without the knowledge of the seed. This blog post proposes an approach to crack Pseudo-Random Number Generators (PRNGs) using machine learning. Creating a machine-learning-resistant version of xorshift128 Using Neural Networks to model the xorshift128 PRNG
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