Open Access
Research Article
Enhancing information security and efficiency through integration of neural networks with RSA Cryptography
*AbhishekCorresponding authorabhisheksaini348@gmail.comUniversity School of Information, Communication & Technology Guru Gobind Singh Indraprastha UniversityDwarka, New Delhi, 110078, IndiaView full profile → , Ruchi Sehrawatruchi.sehrawat@ipu.ac.inUniversity School of Information, Communication & Technology Guru Gobind Singh Indraprastha UniversityDwarka, New Delhi, 110078, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 17 Apr 2024
- Published Online:
- 02 Feb 2026
- 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
Subject Classifications
68M2568P2568P2794A60
References
[1] T. Chandra Segar and R. Vijayaragavan, “Pell’s RSA key generation and its security analysis,” in Proc. 2013 Fourth Int. Conf. Comput., Commun., Netw. Technol. (ICCCNT), pp. 1–5, Jul. (2013).[2] M. Agrawal, N. Kayal, and N. Saxena, “PRIMES is in P,” Ann. Math., pp. 781–793 (2004).[3] Z. K. Abdalrdha, I. H. Al-Qinani, and F. N. Abbas, “Subject review: Key generation in different cryptography algorithms,” Int. J. Sci. Res. Sci., Eng., Technol., vol. 6, no. 5, pp. 230–240 (2019).[4] B. R. Ambedkar and S. S. Bedi, “A new factorization method to factorize RSA public key encryption,” Int. J. Comput. Sci. Issues (IJCSI), vol. 8, no. 6, p. 242 (2011).[5] D. E. Knuth, Art of Computer Programming, Volume 2: Seminumerical Algorithms. Addison-Wesley Professional (2014).[6] L. Dongjiang, W. Yandan, and C. Hong, “The research on key generation in RSA public-key cryptosystem,” in Proc. 2012 Fourth Int. Conf. Comput. Inf. Sci., pp. 578–580, Aug. (2012).[7] L. Egri and T. R. Shultz, “A compositional neural-network solution to prime-number testing,” in Proc. Annu. Meeting Cogn. Sci. Soc., vol. 28, pp. 28–34 (2006).[8] S. V. K. Gaddam and M. Lal, “Efficient cancelable biometric key generation scheme for cryptography,” Int. J. Netw. Secur., vol. 11, no. 2, pp. 61–69 (2010).[9] G. Ganbaatar, D. Nyamdorj, G. Cichon and T.-O. Ishdorj, “Implementation of RSA cryptographic algorithm using SN P systems based on HP/LP neurons,” J. Membr. Comput., vol. 3, pp. 22–34 (2021).[10] P. P. Hadke and S. G. Kale, “Use of neural networks in cryptography: A review,” in Proc. 2016 World Conf. Futuristic Trends Res. Innov. Social Welfare (Startup Conclave), pp. 1–4 (2016).[11] P. Gayathri, S. Umar, G. Sridevi, N. Bashwanth and R. Srikanth, “Hybrid cryptography for random-key generation based on ECC algorithm,” Int. J. Electr. Comput. Eng., vol. 7, no. 3, p. 1293 (2017).[12] S. M. Hussain and H. Al-Bahadili, “A DNA-based cryptographic key generation algorithm,” in Proc. Int. Conf. Secur. Manage. (SAM), pp. 338–340 (2016).[13] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press (2016).[14] A. Jagadeesan and K. Duraiswamy, “Secured cryptographic key generation from multimodal biometrics: Feature level fusion of fingerprint and iris,” arXiv preprint, arXiv:1003.1458 (2010).[15] J. Jin and K. Kim, “3D CUBE algorithm for the key generation method: Applying deep neural network learning-based,” IEEE Access, vol. 8, pp. 33689–33702 (2020).[16] R. M. Jogdand and S. S. Bisalapur, “Design of an efficient neural key generation,” Int. J. Artif. Intell. Appl. (IJAIA), vol. 2, no. 1, pp. 60–69 (2011).[17] W. Kinzel and I. Kanter, “Interacting neural networks and cryptography,” in Advances in Solid State Physics, Springer, pp. 383–391 (2002).[18] G. Meletiou, D. K. Tasoulis, M. N. Vrahatis, et al., “A first study of the neural network approach to the RSA cryptosystem,” in Proc. IASTED 2002 Conf. Artif. Intell., Calgary, pp. 483–488 (2002).[19] I. Meraouche, S. Dutta, H. Tan and K. Sakurai, “Neural networks-based cryptography: A survey,” IEEE Access, vol. 9, pp. 124727–124740 (2021).[20] M. Mumtaz and L. Ping, “Forty years of attacks on the RSA cryptosystem: A brief survey,” J. Discrete Math. Sci. Cryptogr., vol. 22, no. 1, pp. 9–29 (2019).[21] S. A. Nagar and S. Alshamma, “High speed implementation of RSA algorithm with modified keys exchange,” in Proc. 2012 6th Int. Conf. Sci. Electron., Technol. Inf. Telecommun. (SETIT), pp. 639–642 (2012).[22] G. L. Miller, “Riemann’s hypothesis and tests for primality,” J. Comput. Syst. Sci., vol. 13, pp. 300–317 (1976).[23] W. A. Shukur, “A proposed method for generating a private key using digital color image,” Int. J. Appl. Eng. Res., vol. 12, no. 16, pp. 6235–6240 (2017).[24] W. Stallings, Cryptography and Network Security: Principles and Practice. Pearson Education, United Kingdom (2020).[25] W. Samek, G. Montavon, S. Lapuschkin, C. J. Anders and K.-R. Muller, “Explaining deep neural networks and beyond: A review of methods and applications,” Proc. IEEE, vol. 109, no. 3, pp. 247–278 (2021).[26] R. L. Rivest, A. Shamir, and L. Adleman, “A method for obtaining digital signatures and public-key cryptosystems,” Commun. ACM, vol. 21, no. 2, pp. 120–126 (1978).[27] L. Dong and K. Chen “Introduction of cryptographic protocols,” in Cryptographic Protocol: Security Analysis Based on Trusted Freshness, Springer, pp. 1–12 (2012).[28] S. Dua and X. Du, Data Mining and Machine Learning in Cybersecurity. CRC Press, 2016.[29] B. A. Forouzan and D. Mukhopadhyay, Cryptography and Network Security, vol. 12. McGraw-Hill Education (2015).[30] H. Lee and J. Song, “Introduction to convolutional neural network using Keras: An understanding from a statistician,” Commun. Stat. Appl. Methods, vol. 26, no. 6, pp. 591–610 (2019).[31] P. Manuja and R. S. Shekhawat, “Developing information security metrics and measures for risk assessment of an organization,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 25, no. 4, pp. 1195–1202, May (2022), doi: https://doi.org/10.1080/09720529.2022.2075092.[32] K. K. Jabbar, H. A. Hailal, and A. T. Naser, “Information security based on sub-system keys generator by utilizing polynomials method and logic gate,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 26, no. 4, pp. 1135–1143, Jan. (2023), doi: https://doi.org/10.47974/jdmsc-1549.
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