A Blockchain‑Enhanced Deep Learning Approach for Intrusion Detection in Trusted Execution Environments-Scilight

Digital Technologies Research and Applications

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A Blockchain‑Enhanced Deep Learning Approach for Intrusion Detection in Trusted Execution Environments

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https://doi.org/10.54963/dtra.v4i1.962
Aliyu, A. A., Ibrahim, M., & Abdulkadir, S. (2025). A Blockchain‑Enhanced Deep Learning Approach for Intrusion Detection in Trusted Execution Environments. Digital Technologies Research and Applications, 4(1), 135–157. https://doi.org/10.54963/dtra.v4i1.962
Volume 4 Issue 1 (2025): In Progress
Copyright (c) 2025 Ahmed Abubakar Aliyu, Mohammed Ibrahim, Sa’adatu Abdulkadir
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Authors

  • Ahmed Abubakar Aliyu

    Department of Secure Computing, Faculty of Computing, Kaduna State University, Kaduna 800283, Nigeria
  • Mohammed Ibrahim

    Department of Secure Computing, Faculty of Computing, Kaduna State University, Kaduna 800283, Nigeria
  • Sa’adatu Abdulkadir

    Department of Secure Computing, Faculty of Computing, Kaduna State University, Kaduna 800283, Nigeria; Department of Informatics, Faculty of Computing, Kaduna State University, Kaduna 800283, Nigeria

Traditional Intrusion Detection Systems (IDSs) face significant challenges in keeping pace with the rapidly evolving landscape of cyber threats, primarily due to limitations in continuous learning and the accuracy of data classification and analysis. This often results in delayed detection and leaves networks susceptible to severe attacks. This paper introduces an innovative IDS empowered by blockchain technology to mitigate these shortcomings, leveraging continuous learning and self‑adaptive neural networks. The proposed system adopts a proactive approach by continuously assimilating intrusion logs, utilizing a Long Short‑Term Memory (LSTM) core to discern patterns and enhance its real‑time threat detection capabilities, removing a major bottleneck in traditional IDS models by eliminating the need for manual tagging. To further strengthen the security measures, self‑updating neural networks are embedded in each block of the blockchain, forming a decentralized “brain” that evolves defences against even the most sophisticated adversaries. These networks are securely housed in Trusted Execution Environments (TEEs) to maintain operational integrity, enabling tamper‑proof operation and effective threat detection. Real‑world evaluations conducted on the Binance Smart Chain and Ethereum Classic datasets demonstrate the system’s superior performance. With an impressive accuracy rate of 98.50% and a minimal false positive rate of 1.50%, the model demonstrates a remarkable ability to distinguish legitimate network activity from malicious intrusions.

Keywords:

Neural Network Intrusion Detection System Blockchain Deep Learning

References

  1. Rullo, A.; Bertino, E.; Ren, K. Guest Editorial Special Issue on Intrusion Detection for the Internet of Things. IEEE Internet Things J. 2023, 10(10), 8327–8330. DOI: https://doi.org/10.1109/JIOT.2023.3244636
  2. Aliyu, A.A.; Liu, J.; Gilliard, E. Blockchain-Based Poisoning Attack Prevention in Smart Farming. Sci. Practi. Cyber Secur. J. 2023, 7(2), 38–53.
  3. Wang, Z.; Han, D.; Li, M.; et al. The Abnormal Traffic Detection Scheme Based on PCA and SSH. Connect. Sci. 2022, 34(1), 1201–1220. DOI: https://doi.org/10.1080/09540091.2022.2051434
  4. Hephzipah, J.J.; Vallem, R.R.; Sheela, M.S.; et al. An Efficient Cyber Security System Based on Flow-Based Anomaly Detection Using Artificial Neural Network. Mesopotamian J. CyberSecurity 2023, 2023, 48–56. DOI: https://doi.org/10.58496/MJCS/2023/009
  5. Javadpour, A.; Pinto, P.; Ja’fari, F.; et al. DMAIDPS: A Distributed Multi-Agent Intrusion Detection and Prevention System for Cloud IoT Environments. Cluster Comput. 2023, 26(1), 367–384. DOI: https://doi.org/10.1007/s10586-022-03621-3
  6. Li, N.; Zhang, R.; Zhu, C.; et al. A Data Sharing Method for Remote Medical System Based on Federated Distillation Learning and Consortium Blockchain. Connect. Sci. 2023, 35(1), 2186315. DOI: https://doi.org/10.1080/09540091.2023.2186315
  7. Saravanan, V.; Madiajagan, M.; Rafee, S.M.; et al. IoT-Based Blockchain Intrusion Detection Using Optimized Recurrent Neural Network. Multimed. Tools Appl. 2023, 83, 31505–31526. DOI: https://doi.org/10.1007/s11042-023-16662-6
  8. Qu, X.; Liu, Z.; Wu, C.Q.; et al. MFGAN: Multimodal Fusion for Industrial Anomaly Detection Using Attention-Based Autoencoder and Generative Adversarial Network. Sensors 2024, 24(2), 637. DOI: https://doi.org/10.3390/s24020637
  9. Abubakar, A.A.; Liu, J.; Gilliard, E. An Efficient Blockchain-Based Approach to Improve the Accuracy of Intrusion Detection Systems. Electron. Lett. 2023, 59(18), e12888. DOI: https://doi.org/10.1049/ell2.12888
  10. El Houda, Z.A.; Brik, B.; Khoukhi, L. Ensemble Learning for Intrusion Detection in SDN-Based Zero Touch Smart Grid Systems. In Proceeedings of the 2022 IEEE 47th Conference on Local Computer Networks (LCN), Edmonton, AB, Canada, 26–29 September 2022. DOI: https://doi.org/10.1109/LCN53696.2022.9843645
  11. Yang, Y.; Peng, S.; Siet, S.; et al. Detecting Susceptible Communities and Individuals in Hospital Contact Networks: A Model Based on Social Network Analysis. Connect. Sci. 2023, 35(1), 2236810. DOI: https://doi.org/10.1080/09540091.2023.2236810
  12. Kably, S.; Benbarrad, T.; Alaoui, N.; et al. Multi-Zone-Wise Blockchain Based Intrusion Detection and Prevention System for IoT Environment. Comput. Mater. Continu. 2023, 74(1), 253–278. DOI: https://doi.org/10.32604/cmc.2023.032220
  13. Putra, G.D.; Dedeoglu, V.; Pathak, A.; et al. Decentralised Trustworthy Collaborative Intrusion Detection System for IoT. In Proceeedings of the 2021 IEEE International Conference on Blockchain (Blockchain), Melbourne, Australia, 6–8 December 2021. DOI: https://doi.org/10.1109/Blockchain53845.2021.00048
  14. Cai, S.; Han, D.; Yin, X.; et al. A Hybrid Parallel Deep Learning Model for Efficient Intrusion Detection Based on Metric Learning. Connect. Sci. 2022, 34(1), 551–577. DOI: https://doi.org/10.1080/09540091.2021.2024509
  15. Zhou, P.; Zhang, H.; Liang, W. Research on Hybrid Intrusion Detection Based on Improved Harris Hawk Optimization Algorithm. Connect. Sci. 2023, 35(1), 2195595. DOI: https://doi.org/10.1080/09540091.2023.2195595
  16. Chen, J.; Liang, W.; Xiao, L.; et al. PrivBCS: A Privacy-Preserving and Efficient Crowdsourcing System with Fine-Grained Worker Selection Based on Blockchain. Connect. Sci. 2023, 35(1), 2202837. DOI: https://doi.org/10.1080/09540091.2023.2202837
  17. Rahman, Z.; Yi, X.; Khalil, I. Blockchain-Based AI-Enabled Industry 4.0 CPS Protection Against Advanced Persistent Threat. IEEE Internet Things J. 2023, 10(8), 6769–6778. DOI: https://doi.org/10.1109/JIOT.2022.3147186
  18. Alkadi, O.; Moustafa, N.; Turnbull, B. A Review of Intrusion Detection and Blockchain Applications in the Cloud: Approaches, Challenges and Solutions. IEEE Access 2020, 8, 104893–104917. DOI: https://doi.org/10.1109/ACCESS.2020.2999715
  19. Khonde, S.R.; Ulagamuthalvi, V. Hybrid Intrusion detection System Using Blockchain Framework. EURASIP J. Wirel. Commun. Netw. 2022, 2022(1), 58. DOI: https://doi.org/10.1186/s13638-022-02089-4
  20. Rathee, G.; Kerrache, C.A.; Ferrag, M.A. A Blockchain-Based Intrusion Detection System Using Viterbi Algorithm and Indirect Trust for IIoT Systems. J. Sens. Actuator Netw. 2022, 11(4), DOI: https://doi.org/10.3390/jsan11040071
  21. Heidari, A.; Navimipour, N.J.; Unal, M. A Secure Intrusion Detection Platform Using Blockchain and Radial Basis Function Neural Networks for Internet of Drones. IEEE Internet Things J. 2023, 10(10), 8445–8454. DOI: https://doi.org/10.1109/JIOT.2023.3237661
  22. Babu, E.S.; BKN, S.; Nayak, S.R.; et al., Blockchain-Based Intrusion Detection System of IoT urban data with device authentication against DDoS attacks. Comput. Electr. Eng. 2022, 10(3), 108287. DOI: https://doi.org/10.1016/j.compeleceng.2022.108287
  23. Mansour, R.F. Artificial Intelligence Based Optimization with Deep Learning Model for Blockchain Enabled Intrusion Detection in CPS Environment. Sci. Rep. 2022, 12(1), 12937. DOI: https://doi.org/10.1038/s41598-022-17043-z
  24. Aliyu, A.A.; Liu, J. Blockchain-Based Smart Farm Security Framework for the Internet of Things. Sensors 2023, 23(18), 7992. DOI: https://doi.org/10.3390/s23187992
  25. Kumar, R.; Kumar, P.; Tripathi, R.; et al. A Privacy-Preserving-Based Secure Framework Using Blockchain-Enabled Deep-Learning in Cooperative Intelligent Transport System. IEEE Trans. Intell. Transp. Syst. 2022, 23(9), 16492–16503. DOI: https://doi.org/10.1109/TITS.2021.3098636
  26. Janani, K.; Ramamoorthy, S. Threat Analysis Model to Control IoT Network Routing Attacks through Deep Learning Approach. Connect. Sci. 2022, 34(1), 2714–2754. DOI: https://doi.org/10.1080/09540091.2022.2149698
  27. Zheng, J.; Wang, X.; Yang, Q.; et al. A Blockchain-Based Lightweight Authentication and Key Agreement Scheme for Internet of Vehicles. Connect. Sci. 2022, 34(1), 1430–1453. DOI: https://doi.org/10.1080/09540091.2022.2032602
  28. Kumar, P.; Kumar, R.; Garg, S.; et al. A Secure Data Dissemination Scheme for IoT-Based e-Health Systems using AI and Blockchain. In Proceeedings of the GLOBECOM 2022 - 2022 IEEE Global Communications Conference, 4–8 December 2022. DOI: https://doi.org/10.1109/GLOBECOM48099.2022.10000801
  29. Aljabri, A.; Jemili, F.; Korbaa, O. Convolutional Neural Network for Intrusion Detection Using Blockchain Technology. Int. J. Comput. Appl. 2023, 46(2), 67–77. DOI: https://doi.org/10.1080/1206212X.2023.2284443
  30. Kumar, P.; Gupta, G.P.; Tripathi, R. Toward Design of an Intelligent Cyber Attack Detection System using Hybrid Feature Reduced Approach for IoT Networks. Arab. J. Sci. Eng. 2021, 46(4), 3749–3778. DOI: https://doi.org/10.1007/s13369-020-05181-3
  31. Kumar, P.; Kumar, R.; Kumar, A.; et al. Blockchain and Deep Learning for Secure Communication in Digital Twin Empowered Industrial IoT Network. IEEE Trans. Netw. Sci. Eng. 2023, 10(5), 2802–2813. DOI: https://doi.org/10.1109/TNSE.2022.3191601
  32. Kumar, P.; Kumar, R.; Gupta, G.P.; et al. A Blockchain-Orchestrated Deep Learning Approach for Secure Data Transmission in IoT-Enabled Healthcare System. J. Parallel and Distrib. Comput. 2023, 172, 69–83. DOI: https://doi.org/10.1016/j.jpdc.2022.10.002
  33. Kumar, R.; Kumar, P.; Aloqaily, M.; et al. Deep-Learning-Based Blockchain for Secure Zero Touch Networks. IEEE Commun. Mag. 2023, 61(2), 96–102. DOI: https://doi.org/10.1109/MCOM.001.2200294
  34. Kumar, R.; Aljuhani, A.; Kumar, P.; et al. Blockchain-Enabled Secure Communication for Unmanned Aerial Vehicle (UAV) Networks. In DroneCom ’22: Proceedings of the 5th International ACM Mobicom Workshop on Drone Assisted Wireless Communications for 5G and Beyond, New York, NY, USA, 24 October 2022. DOI: https://doi.org/10.1145/3555661.3560861
  35. Gebremariam, G.G.; Panda, J.; Indu, S. Design of Advanced Intrusion Detection Systems Based on Hybrid Machine Learning Techniques in Hierarchically Wireless Sensor Networks. Connect. Sci. 2023, 35(1), 2246703. DOI: https://doi.org/10.1080/09540091.2023.2246703
  36. Cernera, F.; Morgia, M.L.; Mei, A.; et al. Token Spammers, Rug Pulls, and Sniper Bots: An Analysis of the Ecosystem of Tokens in Ethereum and in the Binance Smart Chain (BNB). In Proceedings of the 32nd USENIX Security Symposium, 9–11 August 2023. Available online: https://www.usenix.org/conference/usenixsecurity23/presentation/cernera
  37. Xin, L.; Ziang, L.; Yingli, Z.; et al. TCN Enhanced Novel Malicious Traffic Detection for IoT Devices. Connect. Sci. 2023, 34(1), 1322–1341. Available online: https://www.tandfonline.com/doi/abs/10.1080/09540091.2022.2067124
  38. Song, Y.; Hyun, S.; Cheong, Y.-G. Analysis of Autoencoders for Network Intrusion Detection. Sensors 2021, 21, 4294. DOI: https://doi.org/10.3390/s21134294
  39. Xu, S.; Zhong, J.; Wang, L.; et al. A Privacy-Preserving and Efficient Data Sharing Scheme with Trust Authentication Based on Blockchain for mHealth. Connect. Sci. 2023, 35(1), 2186316. DOI: https://doi.org/10.1080/09540091.2023.2186316
  40. Wang, H.; Li, F. A Text Classification Method Based on LSTM and Graph Attention Network. Connect. Sci. 2022, 34(1), 2466–2480. DOI: https://doi.org/10.1080/09540091.2022.2128047
  41. Maseno, E.M.; Wang, Z.; Xing, H. A Systematic Review on Hybrid Intrusion Detection System. Secur. Commun. Netw. 2022, 2022, 1–23. DOI: https://doi.org/10.1155/2022/9663052
  42. Salzano, F.; Pareschi, R. Enhancing Blockchain Security through Natural Language Processing and Real-Time Monitoring. Int. J. Parallel Emergent Distrib. Syst. 2023, 0(0), 1–16. DOI: https://doi.org/10.1080/17445760.2023.2272280
  43. Hu, J.; Liu, Y.; Wu, K. Neural Network Pruning Based on Channel Attention Mechanism. Connect. Sci. 2022, 34(1), 2201–2218. DOI: https://doi.org/10.1080/09540091.2022.2111405
  44. Jeon, J.; Baek, S.; Jeong, B.; et al. Early Prediction of Ransomware API Calls Behaviour Based on GRU-TCN in Healthcare IoT. Connect. Sci. 2023, 35(1), 2233716. DOI: https://doi.org/10.1080/09540091.2023.2233716
  45. Zhang, S.; Tian, H.; Wang, L.; et al. A Reputation-Based Dynamic Reorganization Scheme for Blockchain Network Sharding. Connect. Sci. 2024, 36(1), 2327438. DOI: https://doi.org/10.1080/09540091.2024.2327438
  46. Sudharsan, R.; Ganesh, E.N. A Swish RNN Based Customer Churn Prediction for the Telecom Industry with a Novel Feature Selection Strategy. Connect. Sci. 2022, 34(1), 1855–1876. DOI: https://doi.org/10.1080/09540091.2022.2083584
  47. Ramraj, S.; Usha, G. Hybrid Feature Learning Framework for the Classification of Encrypted Network Traffic. Connect. Sci. 2023, 35(1), 2197172. DOI: https://doi.org/10.1080/09540091.2023.2197172
  48. Abbas, A.; Khan, M.A.; Latif, S.; et al. A New Ensemble-Based Intrusion Detection System for Internet of Things. Arab. J. Sci. Eng. 2022, 47(2), 1805–1819. DOI: https://doi.org/10.1007/s13369-021-06086-5
  49. Liu, W.; He, Y.; Wang, X.; et al. BFG: Privacy Protection Framework for Internet of Medical Things Based on Blockchain and Federated Learning. Connect. Sci. 2023, 35(1), 2199951. DOI: https://doi.org/10.1080/09540091.2023.2199951
  50. Anthony Jnr, B. Enhancing Blockchain Interoperability and Intraoperability Capabilities in Collaborative Enterprise-a Standardized Architecture Perspective. Enterp. Inf. Syst. 2024, 18(3), 2296647. DOI: https://doi.org/10.1080/17517575.2023.2296647
  51. Jia, Y.; Xiong, L.; Fan, Y.; et al. Blockchain-Based Privacy-Preserving Multi-Tasks Federated Learning Framework. Connect. Sci. 2024, 36(1), 2299103. DOI: https://doi.org/10.1080/09540091.2023.2299103
  52. Shan, W. Digital Streaming Media Distribution and Transmission Process Optimisation Based on Adaptive Recurrent Neural Network. Connect. Sci. 2022, 34(1), 1169–1180. DOI: https://doi.org/10.1080/09540091.2022.2052264