A hybrid blockchain based deep learning model for multivector attack detection in internet of things enabled healthcare systems – Scientific Reports

1. Rejeb, A. et al. The Internet of Things (IoT) in healthcare: Taking stock and moving forward. Internet Things. 22, 100721 (2023).

   Google Scholar 

2. Mustafa, N. & Hussain, K. Threat modeling for healthcare industry security: Safeguarding IoT devices and ensuring business continuity (2024).

3. Li, C., Wang, J., Wang, S. & Zhang, Y. A review of IoT applications in healthcare. Neurocomputing. 565, 127017 (2024).

   Google Scholar 

4. Javaid, M., Haleem, A., Singh, R. P. & Suman, R. Towards insighting cybersecurity for healthcare domains: A comprehensive review of recent practices and trends. Cyber Secur. Appl. 1, 100016 (2023).

   Google Scholar 

5. He, Y., Aliyu, A., Evans, M. & Luo, C. Health care cybersecurity challenges and solutions under the climate of COVID-19: Scoping review. J. Med. Internet Res. 23 (4), e21747 (2021).

   Google Scholar 

6. Naghib, A., Gharehchopogh, F. S. & Zamanifar, A. A comprehensive and systematic literature review on intrusion detection systems in the Internet of Medical Things: current status, challenges, and opportunities. Artif. Intell. Rev. 58 (4), 114 (2025).

   Google Scholar 

7. Yaseen, A. Uncovering evidence of attacker behavior on the network. ResearchBerg Rev. Sci. Technol. 3 (1), 131–154 (2020).

   Google Scholar 

8. Butpheng, C., Yeh, K-H. & Xiong, H. Security and privacy in IoT-cloud-based e-health systems—A comprehensive review. Symmetry. 12 (7), 1191 (2020).

   Google Scholar 

9. Dhar Dwivedi, A., Singh, R., Kaushik, K., Rao Mukkamala, R. & Alnumay, W. S. Blockchain and artificial intelligence for 5G-enabled Internet of Things: Challenges, opportunities, and solutions. Trans. Emerg. Telecommun. Technol. 35(4), e4329 (2024).

10. Gebrezgiher, Y. T., Jeremiah, S. R., Deng, X. & Park, J. H. Machine learning-based blockchain technology for secure V2X communication: Open challenges and solutions. Sensors. 25 (15), 4793 (2025).

    Google Scholar 

11. Krishnamoorthy, S., Dua, A. & Gupta, S. Role of emerging technologies in future IoT-driven Healthcare 4.0 technologies: A survey, current challenges and future directions. J. Ambient Intell. Humaniz. Comput. 14 (1), 361–407 (2023).

    Google Scholar 

12. Bhasker, B. et al. Blockchain framework with IoT device using federated learning for sustainable healthcare systems. Sci. Rep. 15, 26736 (2025).

    Google Scholar 

13. Asante, I. & Wu, L. Blockchain and deep learning for improving impenetrable data passage in the IoT-imbued healthcare system. Cluster Comput. 28 https://doi.org/10.1007/s10586-025-05398-7 (2025).

14. Begum, K., Mozumder, M. A. I., Joo, M. I. & Kim, H. C. BFLIDS: Blockchain-driven federated learning for intrusion detection in IoMT networks. Sens. (Basel). 24 (14), 4591. https://doi.org/10.3390/s24144591 (2024).

    Google Scholar 

15. Alamro, H. et al. Modeling of blockchain-assisted intrusion detection on iot healthcare system using ant lion optimizer with hybrid deep learning. IEEE Access.. https://doi.org/10.1109/ACCESS.2023.3299589 (2023).

16. Katiravan, A. R. S. Enhancing anomaly detection and prevention in Internet of Things (IoT) using deep neural networks and blockchain-based cybersecurity. Sci. Rep. 15, 22369 (2025).

    Google Scholar 

17. Aboshosha, B. W. et al. Enhancing internet of things security in healthcare using a blockchain-driven lightweight hashing system. Beni-Suef Univ. J. Basic. Appl. Sci. 14, 56 (2025).

    Google Scholar 

18. Song, W., Zhu, X., Ren, S., Tan, W. & Peng, Y. A hybrid blockchain and machine learning approach for intrusion detection system in Industrial Internet of Things. Alex. Eng. J. 127, 619–627 (2025).

    Google Scholar 

19. Prasad, V. V. S. H. et al. Blockchain-enhanced distributed denial of service detection in IoT using deep learning and evolutionary computation. Sci. Rep. 15 (1), 22537 (2025).

    Google Scholar 

20. Gueriani, A., Kheddar, H. & Mazari, A. C. Deep reinforcement learning for intrusion detection in IoT: A survey. In The 2nd International Conference on Electronics, Energy and Measurement (IC2EM), vol. 1, 1–7 (IEEE, 2023).

21. Panthakkan, A., Sagherji, L., Alshehabi, M., Shehada, D. J. & Mansoor, W. IoT Guardian: An intelligent framework for multi-class intrusion detection with machine learning. In International IOT, Electronics and Mechatronics Conference, 319–330 (Springer Nature Singapore, 2024).

22. Panahi, O. Secure IoT for healthcare. Eur. J. Innov. Stud. Sustain. 1 (1), 17–23 (2025).

    Google Scholar 

23. Jia, Y., Zhong, F., Alrawais, A., Gong, B. & Cheng, X. Flowguard: An intelligent edge defense mechanism against IoT DDoS attacks. IEEE Internet Things J. 7 (10), 9552–9562 (2020).

    Google Scholar 

24. Gupta, K., Gupta, K. D., Kumar, D., Srivastava, G. & Sharma, D. K. BIDS: Blockchain and intrusion detection system coalition for securing internet of medical things networks. IEEE J. Biomed. Health Inform. https://doi.org/10.1109/JBHI.2023.3325964

25. Hussain, F. et al. Eftim Zdravevski IoT Healthc. Secur. Dataset IEEE Dataport August 13, https://doi.org/10.21227/9w13-2t13 (2021).

26. Neto, E. C. P. et al. CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment. Sensors. 23, 5941 (2023).

27. Vaasudevan, J. et al. A holistic framework for cyber attack detection, classification, and security enhancement of DNP3 protocol in smart grids. IEEE Access. 13, 200177–200195 (2025).

    Google Scholar 

28. Pallakonda, A., Kumar, S. S., Raj, R. D. A., Yanamala, R. M. R. & Prakasha, K. K. Secure and Resilient Cyberattack Detection in ICS Networks: Hybrid Encryption, Protocol Hardening, and Threat Hunting on ELECTRA Modbus Traffic (IEEE Access, 2025).

29. Pallakonda, A. et al. A Unified Cybersecurity Framework for Smart Grids Against Data Integrity Attacks Using Ensemble Learning and Hybrid Encryption (IEEE Access, 2025).

30. Dokku, N. S. et al. Resilient cybersecurity in smart grid ICS communication using BLAKE3-driven dynamic key rotation and intrusion detection. Sci. Rep. 15 (1), 32754 (2025).

    Google Scholar 

31. Pallakonda, A. et al. AI-driven attack detection and cryptographic privacy protection for Cyber-Resilient Industrial Control systems. IoT 6 (3), 56 (2025).

    Google Scholar 

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