Skip to main content
SpringerLink
Log in

Enhancing IoT security: a collaborative framework integrating federated learning, dense neural networks, and blockchain

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The recent expansion of the IoT ecosystem has not only significantly increased connectivity but also introduced new security challenges. To address emerging security challenges, this study proposes a framework that merges the decentralized methodologies of federated learning (FL) and Blockchain. The framework is rigorously tested and validated on the N-BaIoT Dataset employing dense neural networks (DNNs) and logistic regression (LR). This approach decentralizes the training of machine learning (ML) models by distributing the process across individual IoT devices, this enhances the security and privacy of data. The use of Blockchain ensures transparent and secure management of these decentralized models, adding an extra layer of protection against tampering. In addition, this research introduces two novel metrics, namely the Security Efficacy Metric and the Comparative Improvement Factor, which provide a quantitative foundation for evaluating the performance of the proposed framework. The examination of the proposed framework through LR and DNNs demonstrates significant results. The LR model achieved a global accuracy of 99.98%, with an average client data size of 440.95 MB and a model size of 0.00088 MB. Meanwhile, the DNN model exhibited a global accuracy of 99.99%, with an average client data size of 551.95 MB and a model size of 0.09 MB. This research contributes to IoT security by integrating LR and DNNs within the FL setup, complemented by blockchain technology, signifying a substantial advancement in the dynamic IoT ecosystem.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Algorithm 1
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data availability

The dataset utilized in this manuscript is publicly available for research purposes.

References

  1. Vermesan, O., Eisenhauer, M., Sundmaeker, H., Guillemin, P., Serrano, M., Tragos, E.Z., Valiño, J., Gluhak, A., Bahr, R., et al.: Internet of things cognitive transformation technology research trends and applications. In: Cognitive Hyperconnected Digital Transformation, pp. 17–95. River Publishers, Denmark (2022)

    Chapter  Google Scholar 

  2. Ahmed, S., Khan, M.: Securing the internet of things (IoT): a comprehensive study on the intersection of cybersecurity, privacy, and connectivity in the iot ecosystem. AI IoT Fourth Ind. Revol. Rev. 13(9), 1–17 (2023)

    Google Scholar 

  3. Allioui, H., Mourdi, Y.: Exploring the full potentials of IoT for better financial growth and stability: a comprehensive survey. Sensors 23(19), 8015 (2023)

    Article  Google Scholar 

  4. Verma, H., Chauhan, N., Awasthi, L.K.: A comprehensive review of ‘internet of healthcare things’c: networking aspects, technologies, services, applications, challenges, and security concerns. Comput. Sci. Rev. 50, 100591 (2023)

    Article  Google Scholar 

  5. Sarker, I.H., Khan, A.I., Abushark, Y.B., Alsolami, F.: Internet of things (IoT) security intelligence: a comprehensive overview, machine learning solutions and research directions. Mobile Netw. Appl. 28(1), 296–312 (2023). https://doi.org/10.1007/s11036-022-01937-3

    Article  Google Scholar 

  6. Nazir, A., He, J., Zhu, N., Wajahat, A., Ma, X., Ullah, F., Qureshi, S., Pathan, M.S.: Advancing IoT security: a systematic review of machine learning approaches for the detection of iot botnets. J. King Saud Univ. Comput. Info. Sci. 2023, 101820 (2023). https://doi.org/10.1016/j.jksuci.2023.101820

    Article  Google Scholar 

  7. Kouicem, D.E., Bouabdallah, A., Lakhlef, H.: Internet of things security: a top-down survey. Comput. Netw. 141, 199–221 (2018). https://doi.org/10.1016/j.comnet.2018.03.012

    Article  Google Scholar 

  8. Malhotra, P., Singh, Y., Anand, P., Bangotra, D.K., Singh, P.K., Hong, W.-C.: Internet of things: evolution, concerns and security challenges. Sensors 21(5), 1809 (2021). https://doi.org/10.3390/s21051809

    Article  Google Scholar 

  9. Alsabbagh, W., Langendörfer, P.: Security of programmable logic controllers and related systems: today and tomorrow. IEEE Open J. Ind. Electron. Soc. 4, 659 (2023)

    Article  Google Scholar 

  10. Ni, J., Zhang, K., Lin, X., Shen, X.: Securing fog computing for internet of things applications: challenges and solutions. IEEE Commun. Surv. Tutor. 20(1), 601–628 (2017)

    Article  Google Scholar 

  11. Nazir, A., Farooq, A., Nawaz, T., Abbas, R.: Data acquisition and analysis model for e-government. Tech. J. 23(04), 53–59 (2018)

    Google Scholar 

  12. Biswas, S., Sharif, K., Li, F., Nour, B., Wang, Y.: A scalable blockchain framework for secure transactions in IoT. IEEE Internet Things J. 6(3), 4650–4659 (2018)

    Article  Google Scholar 

  13. Bhushan, B., Sahoo, C., Sinha, P., Khamparia, A.: Unification of blockchain and internet of things (BIoT): requirements, working model, challenges and future directions. Wireless Netw. 27, 55–90 (2021)

    Article  Google Scholar 

  14. Nazir, A., Wajahat, A., Akhtar, F., Ullah, F., Qureshi, S., Malik, S.A., Shakeel, A.: Evaluating energy efficiency of buildings using artificial neural networks and k-means clustering techniques. 2020 3rd international conference on computing, mathematics and engineering technologies (iCoMET), 1–7 (2020). https://doi.org/10.1109/iCoMET48670.2020.9073816

  15. Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., Elovici, Y.: N-baIoT-network-based detection of IoT botnet attacks using deep autoencoders. IEEE Pervasive Comput. 17(3), 12–22 (2018)

    Article  Google Scholar 

  16. Ko, A., Fehér, P., Kovacs, T., Mitev, A., Szabó, Z.: Influencing factors of digital transformation: management or it is the driving force? Int. J. Innov. Sci. 14(1), 1–20 (2022)

    Article  Google Scholar 

  17. Popoola, O., Rodrigues, M., Marchang, J., Shenfield, A., Ikpehia, A., Popoola, J.: A critical literature review of security and privacy in smart home healthcare schemes adopting IoT & blockchain: problems, challenges and solutions. Blockchain Res. Appl. 2023, 100178 (2023)

    Article  Google Scholar 

  18. Nazir, A., He, J., Zhu, N., Wajahat, A., Ullah, F., Qureshi, S., Ma, X., Pathan, M.S.: Collaborative threat intelligence: enhancing IoT security through blockchain and machine learning integration. J. King Saud Univ. Comput. Info. Sci. (2024). https://doi.org/10.1016/j.jksuci.2024.101939

    Article  Google Scholar 

  19. Minoli, D., Occhiogrosso, B.: Blockchain mechanisms for IoT security. Internet Thing 1, 1–13 (2018). https://doi.org/10.1016/j.iot.2018.05.002

    Article  Google Scholar 

  20. Shinde, N.K., Seth, A., Kadam, P.: Exploring the synergies: a comprehensive survey of blockchain integration with artificial intelligence, machine learning, and iot for diverse applications. Mach. Learn. Opt. Eng. Design 2023, 85–119 (2023)

    Google Scholar 

  21. Singh, S., Rathore, S., Alfarraj, O., Tolba, A., Yoon, B.: A framework for privacy-preservation of IoT healthcare data using federated learning and blockchain technology. Future Gener. Comput. Syst. 129, 380–388 (2022)

    Article  Google Scholar 

  22. Lu, Y., Huang, X., Dai, Y., Maharjan, S., Zhang, Y.: Blockchain and federated learning for privacy-preserved data sharing in industrial IoT. IEEE Trans. Ind. Info. 16(6), 4177–4186 (2019)

    Article  Google Scholar 

  23. Xu, Y., Lu, Z., Gai, K., Duan, Q., Lin, J., Wu, J., Choo, K.-K.R.: Besifl: blockchain empowered secure and incentive federated learning paradigm in IoT. IEEE Internet Things J. 10(8), 6561–6573 (2021)

    Article  Google Scholar 

  24. Otoum, S., Al Ridhawi, I., Mouftah, H.: Securing critical IoT infrastructures with blockchain-supported federated learning. IEEE Internet Things J. 9(4), 2592–2601 (2021)

    Article  Google Scholar 

  25. Zhang, C., Xu, Y., Elahi, H., Zhang, D., Tan, Y., Chen, J., Zhang, Y.: A blockchain-based model migration approach for secure and sustainable federated learning in IoT systems. IEEE Internet Things J. 10(8), 6574–6585 (2022)

    Article  Google Scholar 

  26. Muthukumar, V., Sivakami, R., Venkatesan, V.K., Balajee, J., Mahesh, T., Mohan, E., Swapna, B.: Optimizing heterogeneity in IoT infra using federated learning and blockchain-based security strategies. Int. J. Comput. Commun. Control 18(6), 10 (2023)

    Article  Google Scholar 

  27. Rahman, M.A., Hossain, M.S., Islam, M.S., Alrajeh, N.A., Muhammad, G.: Secure and provenance enhanced internet of health things framework: a blockchain managed federated learning approach. IEEE Access 8, 205071–205087 (2020)

    Article  Google Scholar 

  28. Cui, L., Qu, Y., Xie, G., Zeng, D., Li, R., Shen, S., Yu, S.: Security and privacy-enhanced federated learning for anomaly detection in IoT infrastructures. IEEE Trans. Ind. Info. 18(5), 3492–3500 (2021)

    Article  Google Scholar 

  29. Salim, S., Turnbull, B., Moustafa, N.: A blockchain-enabled explainable federated learning for securing internet-of-things-based social media 3.0 networks. IEEE Trans. Comput. Soc. Syst. (2021). https://doi.org/10.1109/TCSS.2021.3134463

    Article  Google Scholar 

  30. Jia, B., Zhang, X., Liu, J., Zhang, Y., Huang, K., Liang, Y.: Blockchain-enabled federated learning data protection aggregation scheme with differential privacy and homomorphic encryption in IoT. IEEE Trans. Ind. Info. 18(6), 4049–4058 (2021)

    Article  Google Scholar 

  31. Qi, Y., Hossain, M.S., Nie, J., Li, X.: Privacy-preserving blockchain-based federated learning for traffic flow prediction. Future Generation Comput. Syst. 117, 328–337 (2021)

    Article  Google Scholar 

  32. Xu, Y., Lu, Z., Gai, K., Duan, Q., Lin, J., Wu, J., Choo, K.-K.R.: Besifl: blockchain-empowered secure and incentive federated learning paradigm in IoT. IEEE Internet Things J. 10(8), 6561–6573 (2023). https://doi.org/10.1109/JIOT.2021.3138693

    Article  Google Scholar 

  33. Zhang, C., Xu, Y., Elahi, H., Zhang, D., Tan, Y., Chen, J., Zhang, Y.: A blockchain-based model migration approach for secure and sustainable federated learning in IoT systems. IEEE Internet Things J. 10(8), 6574–6585 (2023). https://doi.org/10.1109/JIOT.2022.3171926

    Article  Google Scholar 

  34. Sun, N., Wang, W., Tong, Y., Liu, K.: Blockchain based federated learning for intrusion detection for internet of things. Front. Comput. Sci. 18(5), 185328 (2024)

    Article  Google Scholar 

  35. Baucas, M.J., Spachos, P., Plataniotis, K.N.: Federated learning and blockchain-enabled fog-iot platform for wearables in predictive healthcare. IEEE Trans. Comput. Soc. Syst. 10, 1732 (2023)

    Article  Google Scholar 

  36. Sezer, B.B., Turkmen, H., Nuriyev, U.: Ppfchain: a novel framework privacy-preserving blockchain-based federated learning method for sensor networks. Internet Things 22, 100781 (2023)

    Article  Google Scholar 

  37. Guduri, M., Chakraborty, C., Margala, M., et al.: Blockchain-based federated learning technique for privacy preservation and security of smart electronic health records. IEEE Trans Consum. Electron (2023). https://doi.org/10.1109/TCE.2023.3315415

    Article  Google Scholar 

Download references

Funding

The work presented in this paper has been supported by the Beijing Natural Science Foundation (No. IS23054).

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Ahsan Nazir, Jingsha He & Nafei Zhu

  2. Department of AI and Software, Gachon University, Seongnam-si, 13120, South Korea

    Muhammad Shahid Anwar

  3. Department of Computer Science, Maynooth University, Kildare, Ireland

    Muhammad Salman Pathan

  4. Innovation Value Institute, Maynooth University, Kildare, Ireland

    Muhammad Salman Pathan

Authors
  1. Ahsan Nazir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jingsha He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nafei Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Muhammad Shahid Anwar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Muhammad Salman Pathan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AN: Played a pivotal role in the conceptualization of the research theme. Was primarily responsible for the original draft writing and the design of the framework. Also contributed significantly to the integration of Federated Learning (FL) algorithms into the research. JH: Assisted in the preparation of the original draft and provided supervision throughout the research process, guiding the project’s direction and focus. NZ: Was instrumental in the review and editing of the manuscript, ensuring the work’s suitability for publication and maintaining the overall quality of the final document. MSA: Reviewed the article and helped in resolving issues raised by the reviewers. MSP: Played his part in setting the structure of the article and correcting the methodology sections.

Corresponding author

Correspondence to Ahsan Nazir.

Ethics declarations

Conflict of interest

The authors declare that they have no Conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nazir, A., He, J., Zhu, N. et al. Enhancing IoT security: a collaborative framework integrating federated learning, dense neural networks, and blockchain. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04436-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10586-024-04436-0

Keywords

Search

Navigation