In the next section, we will discuss how MT works. ![]() To crack the MT algorithm, we need first to examine how it works. ![]() Hence, for the rest of this article, we will focus more on this variant of MT. The most commonly used variant of MT is MT19937. There are many variants of the MT PRNG that follows the same algorithm but differ in the constants and configurations of the algorithm. It can have a very long period of 32bit words with a statistically uniform distribution. MT is by far the most widely used general-purpose (but still: not cryptographically secure) PRNG. The same concept applies to a more complex structured PRNG, namely Mersenne Twister (MT). Although ML had learned the hidden internal structure of the xorshift128 PRNG, it is a very simple PRNG that we used as a proof of concept. In that PRNG, each number is totally dependent on the last four generated numbers (which form the random number internal state). In part 1 of this post, we showed how to train a Neural Network to generate the exact sequence of a relatively simple pseudo-random number generator (PRNG), namely xorshift128.
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