![]() On the other hand, the pseudo-random number generators’ main idea is to generate random sequences and, hence, these sequences should not follow any pattern. Īt first glance, this seemed a bit counter-intuitive as the whole idea behind machine learning algorithms is to learn from the patterns in the data to perform a specific task, ranging from supervised, unsupervised to reinforcement learning. The details of this experiment’s implementation and the best-trained model can be found in. After training, the model can use any consecutive four generated numbers to replicate the same sequence of the PRNG with bitwise accuracy greater than 95%. In the mentioned blog post, the author replicated the xorshift128 PRNG sequence with high accuracy without having the PRNG seed using a deep learning model. 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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