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Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Enhancing information security and efficiency through integration of neural networks with RSA Cryptography

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pp. 579–595Vol. 47Issue 2February 2026DOI: 10.47974/JIOS-1837XML
Received:
17 Apr 2024
Published Online:
23 May 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1837
Pages:
579–595

Abstract

The cryptographic system aims to facilitate the secure exchange of information among the intended users and also avoid unauthorized access. It is critical task to generate cryptographic keys to ensure the security of encrypted information. The widely adopted RSA algorithm relies on prime numbers for public key cryptography. Aim of this paper is to augment the existing RSA key generation process by incorporating neural network model. In recent years use of neural networks has shown great potential to improve the effectiveness and security of cryptographic systems. This paper is based on deep learning based neural networks model, which are trained on large amount of data to learn patterns to optimize and improve the level of unpredictability in the key generation process to enhance security of the RSA. This work also conduct a comprehensive analysis of the key space, entropy and performance evaluation to validate the effectiveness in generating secure keys.

Keywords

CryptographyDeep neural networks (DNN)Key generationRSA

Subject Classifications

68M2568P2568P2794A60

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