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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.08031 |
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| _version_ | 1866910783779110912 |
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| author | Bouke, Mohamed Aly Alramli, Omar Imhemmed Abdullah, Azizol Udzir, Nur Izura Samian, Normalia Othman, Mohamed Hanapi, Zurina Mohd |
| author_facet | Bouke, Mohamed Aly Alramli, Omar Imhemmed Abdullah, Azizol Udzir, Nur Izura Samian, Normalia Othman, Mohamed Hanapi, Zurina Mohd |
| contents | Random number generation plays a vital role in cryptographic systems and computational applications, where uniformity, unpredictability, and robustness are essential. This paper presents the Entropy Mixing Network (EMN), a novel hybrid random number generator designed to enhance randomness quality by combining deterministic pseudo-random generation with periodic entropy injection. To evaluate its effectiveness, we propose a comprehensive assessment framework that integrates statistical tests, advanced metrics, and visual analyses, providing a holistic view of randomness quality, predictability, and computational efficiency. The results demonstrate that EMN outperforms Python's SystemRandom and MersenneTwister in critical metrics, achieving the highest Chi-squared p-value (0.9430), entropy (7.9840), and lowest predictability (-0.0286). These improvements come with a trade-off in computational performance, as EMN incurs a higher generation time (0.2602 seconds). Despite this, its superior randomness quality makes it particularly suitable for cryptographic applications where security is prioritized over speed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08031 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Entropy Mixing Networks: Enhancing Pseudo-Random Number Generators with Lightweight Dynamic Entropy Injection Bouke, Mohamed Aly Alramli, Omar Imhemmed Abdullah, Azizol Udzir, Nur Izura Samian, Normalia Othman, Mohamed Hanapi, Zurina Mohd Cryptography and Security Random number generation plays a vital role in cryptographic systems and computational applications, where uniformity, unpredictability, and robustness are essential. This paper presents the Entropy Mixing Network (EMN), a novel hybrid random number generator designed to enhance randomness quality by combining deterministic pseudo-random generation with periodic entropy injection. To evaluate its effectiveness, we propose a comprehensive assessment framework that integrates statistical tests, advanced metrics, and visual analyses, providing a holistic view of randomness quality, predictability, and computational efficiency. The results demonstrate that EMN outperforms Python's SystemRandom and MersenneTwister in critical metrics, achieving the highest Chi-squared p-value (0.9430), entropy (7.9840), and lowest predictability (-0.0286). These improvements come with a trade-off in computational performance, as EMN incurs a higher generation time (0.2602 seconds). Despite this, its superior randomness quality makes it particularly suitable for cryptographic applications where security is prioritized over speed. |
| title | Entropy Mixing Networks: Enhancing Pseudo-Random Number Generators with Lightweight Dynamic Entropy Injection |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2501.08031 |