Cryptographic Grade Chaotic Random Number Generator Based on Tent-Map

Author:

Al-Daraiseh Ahmad1ORCID,Sanjalawe Yousef2ORCID,Al-E’mari Salam3ORCID,Fraihat Salam4ORCID,Bany Taha Mohammad5ORCID,Al-Muhammed Muhammed1ORCID

Affiliation:

1. Computer Science Department, School of Information Technology, American University of Madaba, Amman 11821, Jordan

2. Cybersecurity Department, School of Information Technology, American University of Madaba, Amman 11821, Jordan

3. Information Security Department, Faculty of Information Technology, University of Petra, Amman 11196, Jordan

4. Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman P.O. Box 346, United Arab Emirates

5. Department of Data Science and Artificial Intelligence, School of Information Technology, American University of Madaba, Amman 11821, Jordan

Abstract

In recent years, there has been an increasing interest in employing chaotic-based random number generators for cryptographic purposes. However, many of these generators produce sequences that lack the necessary strength for cryptographic systems, such as Tent-Map. However, these generators still suffer from common issues when generating random numbers, including issues related to speed, randomness, lack of statistical properties, and lack of uniformity. Therefore, this paper introduces an efficient pseudo-random number generator, called State-Based Tent-Map (SBTM), based on a modified Tent-Map, which addresses this and other limitations by providing highly robust sequences suitable for cryptographic applications. The proposed generator is specifically designed to generate sequences with exceptional statistical properties and a high degree of security. It utilizes a modified 1D chaotic Tent-Map with enhanced attributes to produce the chaotic sequences. Rigorous randomness testing using the Dieharder test suite confirmed the promising results of the generated keystream bits. The comprehensive evaluation demonstrated that approximately 97.4% of the tests passed successfully, providing further evidence of the SBTM’s capability to produce sequences with sufficient randomness and statistical properties.

Publisher

MDPI AG

Subject

Control and Optimization,Computer Networks and Communications,Instrumentation

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