Random Number Generators for Computer Simulation and Cyber Security

Lih-Yuan Deng, PhD, is a Professor in the Department of Mathematical Sciences at the University of Memphis. His research focuses on random number generators and has published several papers on the topic, as well as in other areas of statistics and data science.Nirman Kumar, PhD, is currently a Consulting Member of Technical Staff at Oracle America, Inc. Prior to this, he was an Assistant Professor of Computer Science at the University of Memphis. His research work has focused on theoretical computer science and machine learning and he has several publications in the areas of computational geometry, randomized algorithms, and machine learning.Henry Horng-Shing Lu, PhD, is a Professor in the Institute of Statistics at the National Yang Ming Chiao Tung University. His research focuses on statistics, image science, and bioinformatics with publications on related topics for scientific and industrial applications.Ching-Chi Yang, PhD, is an Assistant Professor in the Department of Mathematical Sciences at the University of Memphis. His research focuses on statistical applications and has published several papers in the areas of statistical dimensional analysis.

• Introduction.- Classical Random Number Generators for Computer Simulation.- Searching for Large Order MRG's.- Methods to Improve Basic Generators and Statistical Justifications.- Family of Efficient MRGs.- Quality Assessment for Random Number Generators.- Spectral Test for Judging MRG Quality.- Parallel RNG Methods.- MRGs with Many Non-zero Terms and its Efficient Implementation.- Empirical Tests on RNG.- Review of Secure Random Number Generators.- Secure RNG for Security Applications.- Review of Classical Ciphers.- Modern Software and Hardware Stream Ciphers.- Design of Secure Random Number Generators.- General Design Principle of New Class of Secure RNGs.- Specific Classes of Secure and Efficient RNGs.- Design and Implementation of R packages.