A Secure and Reliable Architecture for Managing Health Data in the Internet of Things using Blockchain and Deep Learning

Subject Areas : Multimedia Processing, Communications Systems, Intelligent Systems

Behnam Rezaei Bezanjani ¹ , hamid qhafori ^{2 *} , reza gholamrezayi ³

1 - PhD Student, Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran.
2 - Assistant Professor, Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran.
3 - Assistant Professor, Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran

Received: 2024-06-23 Accepted : 2024-08-03 Published : 2024-12-27

Keywords: Blockchain, Privacy-Preserving, Health Care, Machine Learning, ray Wolf Optimization,

Abstract :

Abstract

Introduction: The integration of Internet of Things (IoT) devices in the healthcare sector has brought significant advancements in patient care and data management. These technology-driven innovations hold great promise, while simultaneously raising important security concerns, particularly regarding the protection of medical data against potential cyber threats.

Method: In the initial phase, we leverage blockchain-based request and transaction encryption to enhance the security of data transactions, establishing an immutable and transparent framework.

Resultst: is used for attack classification. We compared the performance of the proposed method with three methods: AIBPSF-IoMT, OMLIDS-PBIoT, and AIMMFIDS. We achieved significant improvements in various metrics: accuracy (1%), precision (1%), recalling (1%).

Discussion: This study presents an innovative approach to addressing the critical challenge of medical data security in the Internet of Things landscape. Our method seamlessly integrates blockchain technology and advanced machine learning techniques, providing a robust framework for enhancing the confidentiality and integrity of sensitive healthcare information.

References:

1. Bezanjani, B. R., Ghafouri, S. H., & Gholamrezaei, R. (2024). Fusion of machine learning and blockchain-based privacy-preserving approach for healthcare data in the Internet of Things. The Journal of Supercomputing, 1-29.‏
2. Saheed, Y. K., & Misra, S. (2024). A voting gray wolf optimizer-based ensemble learning models for intrusion detection in the Internet of Things. International Journal of Information Security, 1-25. .‏
3. Saheed, K.Y., Usman, A.A., Sukat, F.D., & Abdulrahman, M. (2023). A novel hybrid autoencoder and modified particle swarm optimization feature selection for intrusion detection in the internet of things network. Frontiers of Computer Science. .‏
4. Khubrani, M. M. (2023). Artificial Rabbits Optimizer with Deep Learning Model for Blockchain-Assisted Secure Smart Healthcare System. International Journal of Advanced Computer Science and Applications, 14(9). .‏
5. Cai, J., Liang, W., Li, X., Li, K., Gui, Z., & Khan, M. K. (2023). GTxChain: a secure IoT smart blockchain architecture based on graph neural network. IEEE Internet of Things Journal. .‏
6. Kumar, P., Kumar, R., Srivastava, G., Gupta, G. P., Tripathi, R., Gadekallu, T. R., & Xiong, N. N. (2021). PPSF: A privacy-preserving and secure framework using blockchain-based machine-learning for IoT-driven smart cities. IEEE Transactions on Network Science and Engineering, 8(3), 2326-2341. .‏
7. Shrestha, A., & Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE access, 7, 53040-53065. .‏
8. Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., ... & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of big Data, 8, 1-74. .‏
9. Ayyoubzadeh, S. M., Ayyoubzadeh, S. M., Zahedi, H., Ahmadi, M., & Kalhori, S. R. N. (2020). Predicting COVID-19 incidence through analysis of google trends data in Iran: data mining and deep learning pilot study. JMIR public health and surveillance, 6(2), e18828. .‏
10. Ahmad, R. W., Salah, K., Jayaraman, R., Yaqoob, I., Ellahham, S., & Omar, M. (2021). The role of blockchain technology in telehealth and telemedicine. International journal of medical informatics, 148, 104399. .‏
11. Alshammari, B. M. (2023). AIBPSF-IoMT: Artificial Intelligence and Blockchain-Based Predictive Security Framework for IoMT Technologies. Electronics, 12(23), 4806. .‏
12. Afaq, Y., & Manocha, A. (2023). Blockchain and Deep Learning Integration for Various Application: A Review. Journal of Computer Information Systems, 1-14. .‏
13. Shafay, M., Ahmad, R. W., Salah, K., Yaqoob, I., Jayaraman, R., & Omar, M. (2023). Blockchain for deep learning: review and open challenges. Cluster Computing, 26(1), 197-221. .‏
14. Mudassir, M., Unal, D., Hammoudeh, M., & Azzedin, F. (2022). Detection of botnet attacks against industrial IoT systems by multilayer deep learning approaches. Wireless Communications and Mobile Computing, 2022. .‏
15. Azbeg, K., Ouchetto, O., & Andaloussi, S. J. (2022). BlockMedCare: A healthcare system based on IoT, Blockchain and IPFS for data management security. Egyptian Informatics Journal, 23(2), 329-343. .‏
16. Sudhakaran, P. (2022). Energy efficient distributed lightweight authentication and encryption technique for IoT security. International Journal of Communication Systems, 35(2), e4198. .‏
17. Yin, C., Zhang, S., Wang, J., & Xiong, N. N. (2020). Anomaly detection based on convolutional recurrent autoencoder for IoT time series. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(1), 112-122. .‏
18. Xu, Z., Liang, W., Li, K. C., Xu, J., & Jin, H. (2021). A blockchain-based roadside unit-assisted authentication and key agreement protocol for internet of vehicles. Journal of Parallel and Distributed Computing, 149, 29-39. .‏
19. Liang, W., Ning, Z., Xie, S., Hu, Y., Lu, S., & Zhang, D. (2021). Secure fusion approach for the internet of things in smart autonomous multi-robot systems. Information Sciences, 579, 468-482. .‏
20. Khalaf, O. I., & Abdulsahib, G. M. (2021). Optimized dynamic storage of data (ODSD) in IoT based on blockchain for wireless sensor networks. Peer-to-Peer Networking and Applications, 14, 2858-2873. .‏
21. Srinivasu, P. N., Bhoi, A. K., Nayak, S. R., Bhutta, M. R., & Woźniak, M. (2021). Blockchain technology for secured healthcare data communication among the non-terminal nodes in IoT architecture in 5G network. Electronics, 10(12), 1437. .‏
22. Zhang, J. (2021). Distributed network security framework of energy internet based on internet of things. Sustainable Energy Technologies and Assessments, 44, 101051. .‏
23. Zheng, Z., Xie, S., Dai, H. N., Chen, W., Chen, X., Weng, J., & Imran, M. (2020). An overview on smart contracts: Challenges, advances and platforms. Future Generation Computer Systems, 105, 475-491. .‏
24. Li, Y., Chen, C., Liu, N., Huang, H., Zheng, Z., & Yan, Q. (2020). A blockchain-based decentralized federated learning framework with committee consensus. IEEE Network, 35(1), 234-241. .‏
25. Philip, A. O., & Saravanaguru, R. K. (2020). Secure incident & evidence management framework (SIEMF) for internet of vehicles using deep learning and blockchain. Open Computer Science, 10(1), 408-421. .‏
26. AlKadi, O., Moustafa, N., Turnbull, B., & Choo, K. K. R. (2019). Mixture localization-based outliers models for securing data migration in cloud centers. IEEE Access, 7, 114607-114618. .‏
27. Liang, G., Weller, S. R., Luo, F., Zhao, J., & Dong, Z. Y. (2018). Distributed blockchain-based data protection framework for modern power systems against cyber attacks. IEEE Transactions on Smart Grid, 10(3), 3162-3173. .‏
28. Liu, X., Muhammad, K., Lloret, J., Chen, Y. W., & Yuan, S. M. (2019). Elastic and cost-effective data carrier architecture for smart contract in blockchain. Future Generation Computer Systems, 100, 590-599. .‏
29. Rathore, S., & Park, J. H. (2018). Semi-supervised learning based distributed attack detection framework for IoT. Applied Soft Computing, 72, 79-89. .‏
30. Wang, Z. (2018). Deep learning-based intrusion detection with adversaries. IEEE Access, 6, 38367-38384. .‏
31. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67. .‏
32. Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009, July). A detailed analysis of the KDD CUP 99 data set. In 2009 IEEE symposium on computational intelligence for security and defense applications (pp. 1-6). Ieee. .‏
33. Moustafa, N., & Slay, J. (2015, November). UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In 2015 military communications and information systems conference (MilCIS) (pp. 1-6). IEEE. .‏
34. Al-Qarafi, A., Alrowais, F., S. Alotaibi, S., Nemri, N., Al-Wesabi, F. N., Al Duhayyim, M., ... & Al-Shabi, M. (2022). Optimal machine learning based privacy preserving blockchain assisted internet of things with smart cities environment. Applied Sciences, 12(12), 5893. .‏
35. Alohali, M. A., Al-Wesabi, F. N., Hilal, A. M., Goel, S., Gupta, D., & Khanna, A. (2022). Artificial intelligence enabled intrusion detection systems for cognitive cyber-physical systems in industry 4.0 environment. Cognitive Neurodynamics, 16(5), 1045-1057. .‏
36. Hady, A. A., Ghubaish, A., Salman, T., Unal, D., & Jain, R. (2020). Intrusion detection system for healthcare systems using medical and network data: A comparison study. IEEE Access, 8, 106576-106584. .‏
37. Huang, Y., Zhao, Z., & Shi, C. (2024). Network Security Perception System Integrating Improved CNN Algorithm and Improved GRU Algorithm. IEEE Access.‏ .‏
38. Matloob, S. A. Khan, R. Rukaiya, M. A. K. Khattak and A. Munir, “A sequence mining-based novel architecture for detecting fraudulent transactions in healthcare systems”, IEEE Access, vol. 10, pp. 48447- 48463, 2022.
39. Matloob, S. A. Khan, R. Rukaiya, M. A. K. Khattak and A. Munir, “A sequence mining-based novel architecture for detecting fraudulent transactions in healthcare systems”, IEEE Access, vol. 10, pp. 48447- 48463, 2022.S. Dalal, B. Seth, M. Radulescu, C. Secara and C. Tolea, “Predicting fraud in financial payment services through optimized hyperparameter-tuned XGBoost model”, Mathematics, vol. 10, no. 24, 4679, 2021. . .‏
40. I. Matloob, S. A. Khan, R. Rukaiya, M. A. K. Khattak and A. Munir, “A sequence mining-based novel architecture for detecting fraudulent transactions in healthcare systems”, IEEE Access, vol. 10 .‏