Hybrid discrete mathematical and deep learning techniques for nextgeneration cryptographic protocols
*Shikha KhullarCorresponding authorshikha.khullar17@gmail.comDepartment of Computer Science and EngineeringPoornima UniversityJaipur, Rajasthan, 303905, IndiaView full profile → , Rakesh Kumar Saxenarakesh.saxena@poornima.edu.inDepartment of Computer Science and EngineeringPoornima UniversityJaipur, Rajasthan, 303905, IndiaView full profile → , Abhijit Panditabhijitpandit1978@gmail.comDepartment of ManagementSchool of Management and CommerceBrainware UniversityKolkata, West Bengal, 700125, IndiaView full profile → , Vaishali Biradarvaishali.biradar@gmail.comDepartment of Electronics and Telecommunication EngineeringDr. D. Y. Patil Institute of TechnologyPimpri, Pune, Maharashtra, 411018, IndiaView full profile → , Monalisa Sahumonalisa.sahu@vitap.ac.inDepartment of Software and System EngineeringSchool of Computer Science and Engineering (SCOPE)VIT-AP UniversityAmaravati, Andhra Pradesh, 522241, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Dec 2025
- Published Online:
- 14 Aug 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-2692
- Pages:
- 3075–3084
Abstract
This paper involves designing a hybrid cryptography protocol to synthesize the application of discrete modeling in mathematics, and the security and efficiency of the next-generation cryptography protocols are optimized using deep learning. It uses Extended Residue Number Systems (ERNS) to build keys in a nonlinear way, Deep Learning Entropy Enhancer (DLEE) to augment randomness and Neural Cryptographic Optimizer Module (NCOM) to execute modular operations more quickly. Better comparison of results in experimental conditions between synthetic datasets, IoT datasets, and standardized datasets with higher entropy, faster computations and resistance of adversary applications are realized to a greater extent. The findings suggest that it is possible to provide a resilient and scalable platform to the existing cryptography systems using mathematical and flexible AI solutions.
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References
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