Skip to main navigation menu Skip to main content Skip to site footer

Enhancement of IoT Security with Hybrid Cryptosystem of ECC and TinyML Integrated with Blockchain

Abstract

This study addresses the challenge of securing smart-home Internet-of-Things (IoT) systems under severe resource constraints by proposing and evaluating a lightweight hybrid framework that couples on-device anomaly detection (TinyML) with elliptic-curve cryptography (ECC) and blockchain-based event logging. The approach first classifies incoming sensor readings locally using a TinyML anomaly detector (Isolation Forest); normal data are then encrypted with ECC and transmitted, while all security relevant actions  are immutably recorded on a blockchain ledger to provide auditability and device trust. The framework was implemented on a smart home dataset of 49,000 records. The TinyML model achieved strong detection performance (0.98 Precision, 0.97 Recall, 0.975 F1-score, 0.996 Accuracy). Cryptographic and logging overheads were small average ECC key generation in 5.12 ms, encryption 0.85 ms, decryption 0.82 ms and blockchain logging. Overall, the results indicate that combining on device anomaly detection with ECC-secured communication and tamper-evident logging can deliver end-to-end protection, transparency, and scalability for smart-home IoT.

Keywords

ECC, TinyML, Blockchain, Cryptosystem

PDF

References

  1. Nasr, M., et al., Smart healthcare in the age of AI: recent advances, challenges, and future prospects. IEEE access, 2021. 9: p. 145248-145270. https://doi.org/10.1109/ACCESS.2021.3118960.
  2. Choo, K.-K.R. Internet of Things (IoT) security and forensics: Challenges and opportunities. in Proceedings of the 2th Workshop on CPS&IoT Security and Privacy. 2021.https://doi.org/10.1145/3462633.3484691.
  3. Yalli, J.S., M.H. Hasan, and A.A. Badawi, Internet of Things (IoT): origins, embedded technologies, smart applications, and its growth in the last decade. IEEE access, 2024. 12: p. 91357-91382. https://doi.org/10.1109/ACCESS.2024.3418995.
  4. Kuchuk, H. and E. Malokhvii, Integration of IoT with cloud, fog, and edge computing: a review. Advanced Information Systems, 2024. 8(2): p. 65-78.https://doi.org/10.20998/2522-9052.2024.2.08.
  5. Berjis, Z., S.Y. Ameen, and M.H. Al-Jammas. Challenges and Opportunities in Healthcare and Industrial IoT: A Comparative Analysis. in 2024 1st International Conference on Emerging Technologies for Dependable Internet of Things (ICETI). 2024. IEEE.https://doi.org/10.1109/ICETI63946.2024.10777136.
  6. Abbasi, M., E. Mohammadi-Pasand, and M.R. Khosravi, Intelligent workload allocation in IoT–Fog–cloud architecture towards mobile edge computing. Computer Communications, 2021. 169: p. 71-80.https://doi.org/10.1016/j.comcom.2021.01.022.
  7. Ahmed, I., A.K. Ali, and M.S. Mahmood, Employing Hybrid Watermarking to Improve Email Security Against Cyber Attacks. Journal of Soft Computing and Data Mining, 2025. 6(1): p. 435-447. https://doi.org/10.30880/jscdm.2025.06.01.029 .
  8. Amanuel, S.V. and I.M. Ahmed. A Review of the Various Machine Learning Algorithms for Cloud Computing. in 2022 4th International Conference on Advanced Science and Engineering (ICOASE). 2022. IEEE.https://doi.org/10.1109/ICOASE56293.2022.10075592.
  9. Mangla, M., et al. A proposed framework to achieve CIA in IoT networks. in International Conference on Artificial Intelligence and Sustainable Engineering: Select Proceedings of AISE 2020, Volume 2. 2022. Springer.https://doi.org/10.1007/978-981-16-8546-0_3.
  10. Mohammed, S.J. and D.B. Taha, Paillier cryptosystem enhancement for Homomorphic Encryption technique. Multimedia Tools and Applications, 2024. 83(8): p. 22567-22579.https://doi.org/10.1007/s11042-023-16301-0.
  11. Bhattacharjya, A., A holistic study on the use of blockchain technology in CPS and IoT architectures maintaining the CIA triad in data communication. International journal of applied mathematics and computer science, 2022. 32(3): p. 403-413. https://doi.org/10.34768/amcs-2022-0029.
  12. Baker, S.A. and A.S. Nori. A secure proof of work to enhance scalability and transaction speed in blockchain technology for IoT. in 4th international scientific conference of engineering sciences and advances technologies. 2023. aip publishing llc.https://doi.org/10.1063/5.0157213
  13. Al-Hamdani, S. and D.B. Taha. Security in Content Delivery Networks (CDNs): A Literature Review. in 2025 International Conference on Computer Science and Software Engineering (CSASE). 2025. IEEE.https://doi.org/10.1109/CSASE63707.2025.11054035
  14. Agrawal, K., et al., An extensive blockchain based applications survey: tools, frameworks, opportunities, challenges and solutions. IEEE Access, 2022. 10: p. 116858-116906. https://doi.org/10.1109/ACCESS.2022.3219160.
  15. Khan, A.A., et al., Blockchain-enabled infrastructural security solution for serverless consortium fog and edge computing. PeerJ Computer Science, 2024. 10: p. e1933.https://doi.org/10.7717/peerj-cs.1933.
  16. Haval, A.M., Deploying cloud computing and data warehousing to optimize supply chain management and retail analytics, in Applications of Mathematics in Science and Technology. 2025, CRC Press. p. 810-816.https://doi.org/10.1201/9781003606659.
  17. Mohammed, S.J. and Z.N. Al-Kateeb, Chao_SIFT based encryption approach to secure audio files in cloud computing. Multimedia Tools and Applications, 2024: p. 1-15.https://doi.org/10.1007/s11042-024-19424-0
  18. Qazi, R., et al., Security protocol using elliptic curve cryptography algorithm for wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 2021. 12(1): p. 547-566.
  19. Wang, W., et al., EBIAS: ECC-enabled blockchain-based identity authentication scheme for IoT device. High-Confidence Computing, 2025. 5(1): p. 100240. https://doi.org/10.1007/s12652-020-02020-z.
  20. Kadry, H., et al., Intrusion detection model using optimized quantum neural network and elliptical curve cryptography for data security. Alexandria Engineering Journal, 2023. 71: p. 491-500.https://doi.org/10.1016/j.aej.2023.03.072.
  21. Patel, C., et al., EBAKE-SE: A novel ECC-based authenticated key exchange between industrial IoT devices using secure element. Digital Communications and Networks, 2023. 9(2): p. 358-366.https://doi.org/10.1016/j.dcan.2022.11.001.
  22. Katib, I., et al., Safeguarding IoT consumer devices: Deep learning with TinyML driven real-time anomaly detection for predictive maintenance. Ain Shams Engineering Journal, 2025. 16(2): p. 103281.https://doi.org/10.1016/j.asej.2025.103281.
  23. Martinez-Rau, L.S., et al. Tinyml anomaly detection for industrial machines with periodic duty cycles. in 2024 IEEE Sensors Applications Symposium (SAS). 2024. IEEE.https://doi.org/10.1109/SAS60918.2024.10636584.
  24. Antonini, M., et al., An adaptable and unsupervised TinyML anomaly detection system for extreme industrial environments. Sensors, 2023. 23(4): p. 2344.https://doi.org/10.3390/s23042344.
  25. Khan, S., et al., Hybrid computing framework security in dynamic offloading for IoT-enabled smart home system. PeerJ Computer Science, 2024. 10: p. e2211.https://doi.org/10.7717/peerj-cs.2211.
  26. Ulla, M.M., R. Sapna, and R.M. Devadas, Blockchain modeled swarm optimized lyapunov smart contract deep reinforced secure tasks offloading in smart home. MethodsX, 2025. 14: p. 103305.https://doi.org/10.1016/j.mex.2025.103305.
  27. Lu, W., et al., A Deep Learning-Based Text Classification of Adverse Nursing Events. Journal of healthcare engineering, 2021. 2021(1): p. 9800114.https://doi.org/10.1155/2021/9800114.
  28. Joshi, et al. A Secure Hybrid Cloud Enabled architecture for Internet of Things. IEEE access, 2015(2015 IEEE 2nd World Forum on Internet of Things (WF-IoT)).https://doi.org/10.1109/WF-IoT.2015.7389

Downloads

Download data is not yet available.

Similar Articles

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)