Strengthening Data Security in Bioinformatics with Machine Learning and DNA Encryption

Animesh Kairi; Tapas Bhadra; Jannatul Ferdous; Anindya Nag; Walid El-Shafai; Reham Alsabet; Ahmad Taher Azar

doi:10.48084/etasr.17429

Authors

Animesh Kairi Department of Computer Science & Engineering, Institute of Engineering and Management, Kolkata, India
Tapas Bhadra Department of Computer Science & Engineering, Aliah University, Kolkata, India
Jannatul Ferdous Department of Computer Science and Engineering, North Western University, Khulna, Bangladesh
Anindya Nag Department of Computer Science and Engineering, Northern University of Business and Technology, Khulna, Bangladesh
Walid El-Shafai College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia | Automated Systems and Computing Lab (ASCL), Prince Sultan University, Riyadh, Saudi Arabia | Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
Reham Alsabet College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia | Automated Systems and Computing Lab (ASCL), Prince Sultan University, Riyadh, Saudi Arabia
Ahmad Taher Azar College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia | Automated Systems and Computing Lab (ASCL), Prince Sultan University, Riyadh, Saudi Arabia

Volume: 16 | Issue: 2 | Pages: 34327-34336 | April 2026 | https://doi.org/10.48084/etasr.17429

Received: 9 January 2026 | Revised: 29 January 2026, 13 February 2026, and 20 February 2026 | Accepted: 22 February 2026 | Online: 15 March 2026

Corresponding author: Reham Alsabet

Abstract

The explosion of genomic data in bioinformatics has posed great challenges to data confidentiality, integrity, and secure data transmission. Traditional cryptographic techniques are robust but do not always fit well with the biological nature and size of genetic data. This study presents a novel framework for DNA-based cryptography, combined with Machine Learning (ML) for greater bioinformatics data security. The proposed framework combines chaotic DNA encoding with the Random Forest (RF) classifier, guaranteeing security in a real-time adaptive manner. The proposed method achieves strong security and efficiency, with an entropy of 7.99, an avalanche effect of 49.85%, a near-zero correlation coefficient (0.003), a high ML-based attack detection accuracy of 98.4%, and an average encryption/decryption time of approximately 0.95 s. The results show improved resistance to common attacks and increased genomic data storage and transmission efficiency, outperforming conventional approaches such as AES and traditional chaotic DNA methods in terms of entropy, avalanche effect, correlation coefficient, and computational complexity, hence verifying its efficacy for real-world applications.

Keywords:

DNA-based cryptography, bioinformatics, cryptosystem

Downloads

Download data is not yet available.

References

L. M. Adleman, "Molecular Computation of Solutions to Combinatorial Problems," Science, vol. 266, no. 5187, pp. 1021–1024, Nov. 1994.

P. Agarwal, N. Pinnamaneni, and T. Heinis, "Motif caller for sequence reconstruction in motif-based DNA storage," Scientific Reports, vol. 15, no. 1, Nov. 2025, Art. no. 39236.

P. Chithaluru, V. K. Reddy, P. Narsimhulu, and M. Kumar, "DNA computing for the smart wireless sensor networks," in Blockchain and Digital Twin for Smart Healthcare, Elsevier, 2025, pp. 395–417.

R. Annan, J. Noland, K. Perkins, X. Yuan, K. Roy, and L. Qingge, "Genomic privacy and security in the era of artificial intelligence and quantum computing," Discover Computing, vol. 28, no. 1, June 2025, Art. no. 108.

E. Ayday, J. L. Raisaro, and J.-P. Hubaux, "Privacy-Enhancing Technologies for Medical Tests Using Genomic Data," Ecole Polytechnique Federale de Lausanne (EPFL), 2012.

T. V. Galithoti, P. Priyaranjan Nayak, M. R, P. Sudan, P. S, and N. Kaushik, "Enhancing Security In Network Communication Encryption Systems Using Bio-Inspired Algorithm," in 2025 International Conference on Metaverse and Current Trends in Computing (ICMCTC), Apr. 2025, pp. 1–4.

J. Blindenbach, J. Kang, S. Hong, C. Karam, T. Lehner, and G. Gürsoy, "SQUiD: ultra-secure storage and analysis of genetic data for the advancement of precision medicine," Genome Biology, vol. 25, no. 1, Dec. 2024, Art. no. 314.

D. Boneh and C. Dunworth, "Breaking DES using a molecular computer," in DNA Based Computers - Proceedings of a DIMACS Workshop, Apr. 1995, vol. 27, pp. 37–66.

A. Kairi and T. Bhadra, "Decoding the Future Using a Novel DNA-based Cryptosystem," European Chemical Bulletin, vol. 12, no. si10, pp. 3596–3608, Aug. 2023.

C. T. Clelland, V. Risca, and C. Bancroft, "Hiding messages in DNA microdots," Nature, vol. 399, no. 6736, pp. 533–534, June 1999.

A. Gehani, T. LaBean, and J. Reif, "DNA-based Cryptography," in Aspects of Molecular Computing: Essays Dedicated to Tom Head, on the Occasion of His 70th Birthday, N. Jonoska, G. Păun, and G. Rozenberg, Eds. Springer, 2004, pp. 167–188.

R. Pulivarti et al., "Cybersecurity of genomic data," National Institute of Standards and Technology (U.S.), NIST IR 8432, Dec. 2023.

A. Kairi, T. Bhadra, T. Saha, and S. Saha, "Enhancing Healthcare Image Security With DNA Cryptography in the IOT Environment," International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 10, pp. 1975–1986, 2024.

R. Patil and G. HimaBindu, "CuLOA-based Data Encryption with Tuned Key for Privacy Preservation in the Cloud," Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 19546–19552, Feb. 2025.

A. Saini and R. Sehrawat, "Enhancing Data Security through Machine Learning-based Key Generation and Encryption," Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 14148–14154, June 2024.

C. E. Shannon, "Communication theory of secrecy systems," The Bell System Technical Journal, vol. 28, no. 4, pp. 656–715, July 1949.

Y. Makwana, A. Panigrahi, and S. K. Pal, "Complete Key Recovery of a DNA-based Encryption and Developing a Novel Stream Cipher for Color Image Encryption: Bio-SNOW." arXiv, Mar. 10, 2025.

K. Tanaka, A. Okamoto, and I. Saito, "Public-key system using DNA as a one-way function for key distribution," Biosystems, vol. 81, no. 1, pp. 25–29, July 2005.

Q. Yang, Y. Liu, T. Chen, and Y. Tong, "Federated Machine Learning: Concept and Applications," ACM Transactions on Intelligent Systems Technologies, vol. 10, no. 2, Jan. 2019, Art. no. 12.