Statistical attacks on pseudorandom number generators
K. Madhuramadhura.k@manipal.eduDepartment of Information TechnologyManipal Institute of Technology BengaluruManipal Academy of Higher Education (Deemed to be University)Bengaluru, Karnataka, 560064, IndiaView full profile → , *Banothu VijayCorresponding authorBanothuVijay@rediffmail.comCentre for Multidisciplinary ResearchAnurag UniversityHyderabad, Telangana, 500088, IndiaView full profile → , Anisha Chaudharyanisha.chaudhary@quantumeducation.inQuantum University Research CenterQuantum UniversityRoorkee, Uttarakhand, 247667, IndiaView full profile → , Vimal Bibhuvimal.bhibu@niu.edu.inSchool of Engineering & TechnologyNoida International UniversityGreater Noida, Uttar Pradesh, 201312, IndiaView full profile → , Sardor Buriyevsardor_buriyev@tues.uzDepartment of Information Technology and Exact SciencesTermez University of Economics and ServiceTermez, 190111, UzbekistanView full profile → , Mutabar Latipovalatipovm1991@gmail.comTashkent Institute of Irrigation and Agricultural Mechanization EngineersNational Research UniversityKori Niyoziy 39, Tashkent, 100000, UzbekistanView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2026
- Published Online:
- 31 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-3047
- Pages:
- 4237–4247
Abstract
The statistical attacks on pseudorandom number generators (PRNGs) expose vulnerability of the deterministic nature of the structure of the generators. This paper discusses mathematical concepts which determine how PRNG works and the statistical vulnerability of PRNG to frequency and block frequency tests. Moreover, pattern recognition methods that are based on machine learning are used to extract latent correlations and predictability in the generated sequences. Uniformity, independence and entropy are the major statistical properties which are systematically tested to ascertain the extent of randomness. As empirical evidence indicates, state-of-the-art PRNGs have predictable biases, and a rigorous statistical analysis, and that machine learning-based models can enhance the effectiveness of attacks, uncovering the latent trends. The comparative analysis indicates that performance variations in terms of testing techniques and learning models exist. The findings suggest that there ought to be effective design policies and improved randomness validation schemes to minimize the statistical attacks and improve the security of cryptography.
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References
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