Efficient On-Chip Randomness Testing Utilizing Machine Learning Techniques.
|Year of publication||2019|
|Type||Article in Periodical|
|Magazine / Source||IEEE Transactions on Very Large Scale Integration (VLSI) Systems|
|MU Faculty or unit|
|Keywords||Testing; Cryptography; Field programmable gate arrays; Hardware; System-on-chip; Generators; Machine learning|
|Description||Randomness testing is an important procedure that bit streams, produced by critical cryptographic primitives such as encryption functions and hash functions, have to undergo. In this paper, a new hardware platform for the randomness testing is proposed. The platform exploits the principles of genetic programming, which is a machine learning technique developed for the automated program and circuit design. The platform is capable of evolving efficient randomness distinguishers directly on a chip. Each distinguisher is represented as a Boolean polynomial in the algebraic normal form. The randomness testing is conducted for bit streams that are either stored in an on-chip memory or generated by a circuit placed on the chip. The platform is developed with a Xilinx Zynq-7000 All Programmable System on Chip that integrates a field programmable gate array with on-chip ARM processors. The platform is evaluated in terms of the quality of randomness testing, performance, and resources utilization. With power budget less than 3 W, the platform provides comparable randomness testing capabilities with the standard testing batteries running on a personal computer.|