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1. BEST RANDOM NUMBER GENERATOR ALGORITHM HOW TO
2. BEST RANDOM NUMBER GENERATOR ALGORITHM UPDATE
3. BEST RANDOM NUMBER GENERATOR ALGORITHM CODE
4. BEST RANDOM NUMBER GENERATOR ALGORITHM CRACK

So after understanding the algorithm, we can see the simple relation between the last four generated numbers and the next one. Of course, we need y and z to generate the random numbers afterward, but the value of o depends only on x and w. We can notice from this algorithm that we only need w and x to generate the next random number output. The schematic diagram of this algorithm is shown below: Where O i is the i th bit of the output, and the sign ^ is a bitwise XOR.

Using this code, we can derive the logical expression of each bit of the output, which leads us to the following output bits representations of the output, o: The new w is then returned as the new random number output of the random number generator. The new value of w is evaluated by applying some bit manipulations (shifts and XORs) to the old values of x and w. Every time we call the generator, it will shift the internal variables as follows: y → x, z → y, and w → z.

BEST RANDOM NUMBER GENERATOR ALGORITHM UPDATE

Of course, we can update this generator implementation to provide the seed with a different function call instead of hardcoding it.

It has four internal numbers, x, y, z, and w, representing the seed and the state of the PRNG simultaneously. Here is the function that was used in the repo to generate the PRNG sequence (which is used to train the ML model): def xorshift128():Īs we can see from the code, the implementation is straightforward.

BEST RANDOM NUMBER GENERATOR ALGORITHM CODE

The code implementation of this algorithm can be found on Wikipedia and shared in. Let’s start by digging into its implementation of the xorshift128 PRNG algorithm to learn how it works.

BEST RANDOM NUMBER GENERATOR ALGORITHM CRACK

To understand whether machine learning (ML) could crack the xorshift128 PRNG, we need to comprehend how it works and check whether it is following any patterns or not.

Here, we show our work in beginning to explore this space. ** Editor’s Note: How does this relate to security? While this research looks at a non-cryptographic PRNG, we are interested, generically, in understanding how deep learning-based approaches to finding latent patterns within functions presumed to be generating random output could work, as a prerequisite to attempting to use deep learning to find previously-unknown patterns in cryptographic (P)RNGs, as this could potentially serve as an interesting supplementary method for cryptanalysis of these functions. Let’s start first by examining the xorshift128 algorithm. So, how is this possible? And why can machine learning crack the PRNG? Can we even get better than the 95% bitwise accuracy? That is what we are going to discuss in the rest of this article. Not only learn, but also get a 95% bitwise accuracy, which means that the model will generate the PRNG’s exact output and only gets, on average, two bits wrong. So, it did not make any sense (at the beginning) to train an ML model, which learns from the data patterns, from PRNG that should not follow any pattern. 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.

BEST RANDOM NUMBER GENERATOR ALGORITHM HOW TO

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