Exploring Patterns and Assessing the Security of Pseudorandom Number Generators with Machine Learning

Sara Boancă

2024

Abstract

In recent years, Machine Learning methods have been employed for testing the security of pseudorandom number generators. It is considered that successful learning from pseudorandom data implies the existence of some detectable pattern within it, thus reducing the generator security. As the number and complexity of such approaches has reported important growth, the aim of the present paper is to synthesize current results, discuss perspectives and challenges and provide relevant guidelines for future study. To the best of our knowledge, this is the first comprehensive analysis on the current state of the research into the problem of pseudorandomness exploration by means of Machine Learning.

Paper Citation

in Harvard Style

Boancă S. (2024). Exploring Patterns and Assessing the Security of Pseudorandom Number Generators with Machine Learning. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 186-193. DOI: 10.5220/0012312900003636

in Bibtex Style

@conference{icaart24,
author={Sara Boancă},
title={Exploring Patterns and Assessing the Security of Pseudorandom Number Generators with Machine Learning},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={186-193},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012312900003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Exploring Patterns and Assessing the Security of Pseudorandom Number Generators with Machine Learning
SN - 978-989-758-680-4
AU - Boancă S.
PY - 2024
SP - 186
EP - 193
DO - 10.5220/0012312900003636
PB - SciTePress