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Analyzing efficiency of Pseudo-Random Number Generators using Machine Learning

Random numbers are very important components in cyber security. A main application of random numbers is in the field of cryptography. PKI and TLS based encryption uses random numbers extensively. Other areas include session IDs of web applications, passwords and game of chance. A number of cyber security attacks have happened in the past because of using weak random number generators.
 Machine learning has been extensively used in pattern identification in a number of areas, including credit card frauds, genomics and face identification. It utilizes a set of algorithms to detect patterns from in a data to perform tasks like classification, clustering and prediction.
 In this paper, a fast method to test the randomness of a sequence generated by pseudo-random number generator is proposed which tries to take advantages of the pattern identification of machine learning.