Symmetric cryptographic systems enhanced with AI-aided group theory models

Welekar, Rashmi; Naik, Ketaki; Kute, Dinesh; Mamadiyarov, Zokir; Goyal, Shweta; Parjane, Vaibhav Arjunrao

doi:10.47974/JDMSC-2517

Open Access Research Article

Symmetric cryptographic systems enhanced with AI-aided group theory models

Rashmi Welekarwelekarr@rknec.eduDepartment of Information Technology and SecurityRamdeobaba UniversityNagpur, Maharashtra, 440013, IndiaView full profile → , ^*Ketaki NaikCorresponding authorketaki.naik@bharatividyapeeth.eduDepartment of Information TechnologyBharati Vidyapeeth’s College of Engineering for WomenPune, Maharashtra, 411043, IndiaView full profile → , Dinesh Kutedineshkute90@gmail.comDepartment of Applied Sciences & HumanitiesPimpri Chinchwad College of EngineeringPune, Maharashtra, 411044, IndiaView full profile → , Zokir Mamadiyarovmamadiyarov_zokir@mamunedu.uz zokir_mamadiyarov@tues.uz z.mamadiyarov@tsue.uzAffiliation 1Department of EconomicsMamun UniversityKhiva, 220900, UzbekistanAffiliation 2Department of Finance and TourismTermez University of Economics and ServiceTermez, 190111, UzbekistanAffiliation 3Department of Bank Accounting and AuditingTashkent State University of Economics100066, Tashkent, UzbekistanView full profile → , Shweta Goyalshwetugoyal@gmail.comDepartment of Electrical EngineeringGraphic Era Deemed to be UniversityDehradun, Uttarakhand, 248002, IndiaView full profile → , Vaibhav Arjunrao Parjaneparjanevaibhav77@gmail.comDepartment of Computer TechnologySanjivani K.B.P. PolytechnicKopargaon, Maharashtra, 423603, IndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 711–720Vol. 29Issue 2-AFebruary 2026DOI: 10.47974/JDMSC-2517 Crossmark XML

Received:: 07 May 2025
Published Online:: 03 Feb 2026
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2517
Pages:: 711–720

Abstract

By adding AI-driven group theory models to symmetric encryption systems, this study shows a new way to make them better. We create AI programs that look at complicated group structures to make it easier to make symmetric keys, which make cryptography more secure. The method uses mathematical ideas helped by AI to improve security without slowing down the encryption and decoding processes. AI-driven changes to algorithms allow dynamic adaptability to changing threat models, which makes the encryption and key creation processes stronger. We describe in detail the system design that includes these AI-group theory parts, showing how they can be easily added to existing symmetric encryption systems.

Keywords

Symmetric cryptography AI-driven group theory Key generation Encryption algorithms Cryptographic robustness

Subject Classifications

31A1033E30

References

[1] S. Kumar, W. M. Lim, U. Sivarajah, and J. Kaur, “Artificial intelligence and blockchain integration in business: Trends from a bibliometric-content analysis,” Inf. Syst. Front., vol. 25, pp. 871–896 (2023).
[2] N. A. Perifanis and F. Kitsios, “Investigating the influence of artificial intelligence on business value in the digital era of strategy: A literature review,” Information, vol. 14, pp. 85 (2023).
[3] W. Nasar, R. Da Silva Torres, O. E. Gundersen, and A. T. Karlsen, “The use of decision support in search and rescue: A systematic literature review,” ISPRS Int. J. Geo-Inf., vol. 12, pp. 182 (2023).
[4] X. Liu, X. Wang, J. Chang, H. Zhang, and P. Cao, “Landscape analysis and overview of the literature on oxidative stress and pulmonary diseases,” Front. Pharmacol., vol. 14, pp. 1190817 (2023).
[5] M. Gauffriau, “Counting methods introduced into the bibliometric research literature 1970–2018: A review,” Quant. Sci. Stud., vol. 2, pp. 932–975 (2021).
[6] W. Jiang, P. S. Sousa, M. R. Moreira, and G. M. Amaro, “Lean direction in literature: A bibliometric approach,” Prod. Manuf. Res., vol. 9, pp. 241–263 (2021).
[7] N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim, “How to conduct a bibliometric analysis: An overview and guidelines,” J. Bus. Res., vol. 133, pp. 285–296 (2021).
[8] A. Bunjak, M. Černe, and E. L. Schölly, “Exploring the past, present, and future of the mindfulness field: A multitechnique bibliometric review,” Front. Psychol., vol. 13, pp. 792599 (2022).
[9] X. Fang, H. Ren, and M. Gu, “Orbital angular momentum holography for high-security encryption,” Nat. Photon., vol. 14, pp. 102–108 (2020).
[10] P. J. Taylor, T. Dargahi, A. Dehghantanha, R. M. Parizi, and K. K. R. Choo, “A systematic literature review of blockchain cyber security,” Digit. Commun. Netw., vol. 6, pp. 147–156 (2020).
[11] S. Limkar, A. Wankhede, S. Singh, A. Singh, S. K. Wagh, and S. N. Ajani, “A mechanism to ensure identity-based anonymity and authentication for IoT infrastructure using cryptography,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 26, no. 5, pp. 1597–1611 (2023), doi: 10.47974/JDMSC-1827.
[12] B. Li, Y. Feng, Z. Xiong, W. Yang, and G. Liu, “Research on AI security enhanced encryption algorithm of autonomous IoT systems,” Inf. Sci., vol. 575, pp. 379–398 (2021).
[13] S. H. Kulkarni, V. V. Deshmukh, R. K. Moje, and S. H. Lavate, “Applied discrete mathematics in developing efficient cryptographic algorithms for enhancing information security in WSN,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 5-A, pp. 1733–142 (2025).

Views: 160Downloads: 83Citations: 0