Can machine learning analyze random number generator? I'm studying for random number generators(RNG) and I saw about machine learning a few days ago. 
So I searched analysis of RNG using machine learning. 
But I couldn't find such fields. 
Are there such examples for analysis of RNG using machine learning?
No matter some papers of articles.
The goal of an ideal cryptographically secure pseudo-random number generator (CSPRNG) is to produce a stream of numbers that no machine can distinguish from a truly random stream of numbers. Formally, it's impossible unknown whether it's possible to prove that a CSPRNG is truly random.

That being said, there exists a family of statistical tests that can measure whether a sequence of numbers appears to be uniformly random, and produce a probabilistic analysis as output. I'm not sure if these tests qualify as related to machine learning, but here are some examples:


Chi-squared distribution testing.
Statistical hypothesis testing.


If you are interested in trying out a CSPRNG, you can use:


AES-CTR with a random key acting as the seed.
Under Linux, Mac OS X, BSD, /dev/urandom.
window.crypto.getRandomValues() in your browser console (Chrome, Firefox).


Note that not all PRNGs are CSPRNGs.