Acta Informatica Pragensia 2025, 14(3), 316-339 | DOI: 10.18267/j.aip.2602878

EBSSPA: Efficient Deep Learning Model for Enhancing Blockchain Scalability and Security Through Fusion Pattern Analysis

Anuradha Hiwase ORCID...1, Amit Pimpalkar ORCID...2,3, Barkha Dange ORCID...3, Nitin Thakre ORCID...4, Sakshi Jaiswal ORCID...5, Tejaswini Mankar ORCID...6
1 Department of Computer Science and Engineering, Yashwantrao Chavan College of Engineering, Nagpur, Maharashtra, India
2 Department of Computer Science and Engineering, Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India
3 School of Computer Science and Engineering, Ramdeobaba University, Nagpur, Maharashtra, India
4 Department of Computer Science and Engineering, Govindrao Wanjari College of Engineering and Technology, Nagpur, Maharashtra, India
5 Department of Computer Science and Engineering, Indian Institute of Information Technology, Nagpur, Maharashtra, India
6 Department of Computer Science and Engineering, KDK College of Engineering, Nagpur, Maharashtra, India

Background: Blockchain technologies have come a long way, and integration of blockchain technologies into different fields is flourishing; however, there is a lack of blockchain platforms to manage the high network loads and more sophisticated security threats. These limitations impede the mass adoption of blockchain applications. One of the main reasons blockchain needs artificial intelligence (AI) is to integrate it for the widespread adoption of blockchain technology, as AI addresses scalability and security problems.

Objective: The article proposes a pattern analysis model to overcome scalability and security limitations in blockchain systems by applying advanced AI techniques.

Methods: To make the model scalable, the proposed model uses deep learning methods such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. Furthermore, random forest and convolutional neural networks (CNNs) are applied to augment security operations as an effective classier and anomaly detector on transaction data and a real-time threat detection on transaction patterns using the CNNs. By analysing time series data and dealing with long-term dependencies, the model uses RNNs and LSTMs to enable the strategic introduction of the model to predict and control network loads.

Results: When the proposed model is tested against a curated cloud dataset, it significantly outperforms the state-of-the-art approach in all the performance parameters. More specifically, it has exhibited a 5.05% increase in processing speed, 8.05% improvement in energy efficiency, and 5.27%, 5.8%, 10.24% and 11.62% better attack analysis precision, accuracy, recall and AUC, respectively.

Conclusion: The synergistic interaction of the applied AI techniques results in a blockchain paradigm that is both scalable and resilient to new security threats. This significant improvement in performance parameters demonstrates the effectiveness of integrating AI with blockchain technology to overcome scalability and security limitations, thereby enabling the widespread adoption of blockchain applications.

Keywords: Artificial intelligence; Blockchain technology; Scalability; Machine learning; Network congestion; Network load prediction; Real-time threat detection.

Received: August 15, 2024; Revised: January 13, 2025; Accepted: January 31, 2025; Prepublished online: March 8, 2025; Published: August 19, 2025  Show citation

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Hiwase, A., Pimpalkar, A., Dange, B., Thakre, N., Jaiswal, S., & Mankar, T. (2025). EBSSPA: Efficient Deep Learning Model for Enhancing Blockchain Scalability and Security Through Fusion Pattern Analysis. Acta Informatica Pragensia, 14(3), 316-339. doi: 10.18267/j.aip.260
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    References

    1. Ali, A., Ali, H., Saeed, A., Ahmed Khan, A., Tin, TT, Assam, M., Ghadi, Y.Y., & Muhammad, H.G. (2023). Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning. Sensors, 23, 7740. https://doi.org/10.3390/s23187740 Go to original source...
    2. Alsamhi, S. H., Hawbani, A., Sahal, R., Srivastava, S., Kumar, S., Zhao, L., Al-Qaness, M. A., Hassan, J., Guizani, M., & Curry, E. (2024). Towards sustainable industry 4.0: A survey on greening IoE in 6G networks. Ad Hoc Networks, 165, 103610. https://doi.org/10.1016/j.adhoc.2024.103610 Go to original source...
    3. Alsamhi, H., Shvetsov, V., Shvetsova, V., Hawbani, A., Guizani, M., Alhartomi, A. & Ma, O. (2023). Blockchain-Empowered Security and Energy Efficiency of Drone Swarm Consensus for Environment Exploration. IEEE Transactions on Green Communications and Networking, 7(1), 328-338. https://doi.org/10.1109/TGCN.2022.3195479 Go to original source...
    4. Arabsorkhi, A., & Ebrahimi, S. (2022). Blockchain Applications for the Police Task Force of IRI: A Conceptual Framework Using Fuzzy Delphi Method. Journal of Information Technology Management, 14, 36-61. https://doi.org/10.22059/jitm.2022.87840 Go to original source...
    5. Bagchi, P., Maheshwari, R., Bera, B., Das, A. K., Park, Y., Lorenz, P., & Yau, D. K. Y. (2023). Public Blockchain-Envisioned Security Scheme using post Quantum Lattice-Based aggregate signature for internet of Drones applications. IEEE Transactions on Vehicular Technology, 72(8), 10393-10408. https://doi.org/10.1109/tvt.2023.3260579 Go to original source...
    6. Bathula, A., Gupta, S. K., Merugu, S., Saba, L., Khanna, N. N., Laird, J. R., Sanagala, S. S., Singh, R., Garg, D., Fouda, M. M., & Suri, J. S. (2024). Blockchain, artificial intelligence, and healthcare: the tripod of future-a narrative review. Artificial Intelligence Review, 57(9), 238. https://doi.org/10.1007/s10462-024-10873-5 Go to original source...
    7. Bendiab, G., Hameurlaine, A., Germanos, G., Kolokotronis, N. & Shiaeles, S. (2023). Autonomous Vehicles Security: Challenges and Solutions Using Blockchain and Artificial Intelligence. IEEE Transactions on Intelligent Transportation Systems, 24(4), 3614-3637. https://doi.org/10.1109/TITS.2023.3236274 Go to original source...
    8. Bukola F. B., Khushboo T., Shrikant T., Shyam M. S., & Amit K. T. (2024). A blockchain-based deep learning approach for cyber security in next-generation medical cyber-physical systems. Journal of Autonomous Intelligence, 7(5), 1-18. https://doi.org/10.32629/jai.v7i5.1478 Go to original source...
    9. 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, 10(24), 21502-21514. https://doi.org/10.1109/JIOT.2023.3296469 Go to original source...
    10. Chen, X., Yang, A., Weng, J., Tong, Y., Huang, C., & Li, T. (2023). A Blockchain-Based Copyright Protection Scheme With Proactive Defense. IEEE Transactions on Services Computing, 16(4), 2316-2329. https://doi.org/10.1109/TSC.2023.3246476 Go to original source...
    11. Costa, L. D., Pinheiro, B., Cordeiro, W., Araújo R., & Abelém, A. (2023). Sec-Health: A Blockchain-Based Protocol for Securing Health Records. IEEE Access, 11, 16605-16620. https://doi.org/10.1109/ACCESS.2023.3245046 Go to original source...
    12. Dai, W., Liu, J., Zhou, Y., Choo, K. R., Xie, X., Zou, D., & Jin, H. (2024). PRBFPT: A Practical Redactable Blockchain Framework With a Public Trapdoor. IEEE Transactions on Information Forensics and Security, 19, 2425-2437. https://doi.org/10.1109/TIFS.2024.3349855 Go to original source...
    13. Das, D., Banerjee, S., Chatterjee, P., Ghosh U., & Biswas, U. (2023). A Secure Blockchain Enabled V2V Communication System Using Smart Contracts. IEEE Transactions on Intelligent Transportation Systems, 24(4), 4651-4660. https://doi.org/10.1109/TITS.2022.3226626 Go to original source...
    14. Duan, L., Sun, Y., Ni, W., Ding, W., Liu, J., & Wang, W. (2023). Attacks Against Cross-Chain Systems and Defense Approaches: A Contemporary Survey. CAA Journal of Automatica Sinica, 10(8), 1647-1667. https://doi.org/10.1109/JAS.2023.123642 Go to original source...
    15. Echikr, A., Yachir, A., Kerrache, C. A., Oudjida, A. K., & Sahraoui, Z. (2024). Interoperable IoRT for Healthcare: Securing Intelligent Systems with Decentralized Blockchain. Acta Informatica Pragensia, 13(2), 168-192. https://doi.org/10.18267/j.aip.233 Go to original source...
    16. Feng, Q., Yang, K., Ma, M., & He, D. (2023). Efficient Multi-Party EdDSA Signature With Identifiable Aborts and its Applications to Blockchain. IEEE Transactions on Information Forensics and Security, 18, 1937-1950. https://doi.org/10.1109/TIFS.2023.3256710 Go to original source...
    17. Haritha, T., & Anitha, A. (2023). Multi-Level Security in Healthcare by Integrating Lattice-Based Access Control and Blockchain-Based Smart Contracts System. IEEE Access, 11, 114322-114340. https://doi.org/10.1109/ACCESS.2023.3324740 Go to original source...
    18. Jiaxing, L., Jigang, W., Lin J., & Jin L. (2024). Blockchain-based public auditing with deep reinforcement learning for cloud storage. Expert Systems With Applications, 242, 122764. https://doi.org/10.1016/j.eswa.2023.122764 Go to original source...
    19. Jie, W., Qiu, W., Voundi Koe A., Li, J., Wang, Y., Wu, Y., Li, J., Zheng Z. (2024). A Secure and Flexible Blockchain-Based Offline Payment Protocol. IEEE Transactions on Computers, 73(2), 408-421. https://doi.org/10.1109/TC.2023.3331823 Go to original source...
    20. Kuznetsov, O., Sernani, P., Romeo, L., Frontoni, E., & Mancini, A. (2024). On the Integration of Artificial Intelligence and Blockchain Technology: A Perspective About Security. IEEE Access, 12, 3881-3897. https://doi.org/10.1109/ACCESS.2023.3349019. Go to original source...
    21. Li, W., Zhao, Z., Ma, P., Xie, Z., Palade, V., & Liu, H. (2024). Graphical Consensus-Based sharding for efficient and secure sharings in Blockchain-Enabled internet of vehicles. IEEE Transactions on Vehicular Technology, 73(2), 1991-2002. https://doi.org/10.1109/tvt.2023.3311445 Go to original source...
    22. Liu, J., Jiang, W., Sun, R., Bashir, A. K., Alshehri, M. D., Hua, Q., Yu, K. (2023). Conditional Anonymous Remote Healthcare Data Sharing Over Blockchain. IEEE Journal of Biomedical and Health Informatics, 27(5), 2231-2242. https://doi.org/10.1109/JBHI.2022.3183397 Go to original source...
    23. Olumide, O. M., Danda, B. R., & Moses, G. (2018). Next-generation cybersecurity through a blockchain-enabled federated cloud framework. The Journal of Supercomputing, 74, 10, 5099-5126. https://doi.org/10.5555/3288339.3288359 Go to original source...
    24. Peng, G., Zhang, A., & Lin, X. (2023). Patient-Centric Fine-Grained Access Control for Electronic Medical Record Sharing With Security via Dual-Blockchain. IEEE Transactions on Network Science and Engineering, 10(6), 3908-3921. https://doi.org/10.1109/TNSE.2023.3276166 Go to original source...
    25. Puneeth, R. P., & Parthasarathy, G. (2024). Blockchain-Based Framework for Privacy Preservation and Securing EHR with Patient-Centric Access Control. Acta Informatica Pragensia, 13(1), 1-23. https://doi.org/10.18267/j.aip.225 Go to original source...
    26. Qin, H., Tan, Y., Chen, Y., Ren W., & Choo, K. (2024). TriBoDeS: A Tri-Blockchain-Based Detection and Sharing Scheme for Dangerous Road Condition Information in Internet of Vehicles. IEEE Internet of Things Journal, 11(2), 3563-3577. https://doi.org/10.1109/JIOT.2023.3297259 Go to original source...
    27. Rani, S., Babbar, H., Srivastava, G., Gadekallu T. R., & Dhiman, G. (2023). Security Framework for Internet-of-Things-Based Software-Defined Networks Using Blockchain. IEEE Internet of Things Journal, 10(7), 6074-6081. https://doi.org/10.1109/JIOT.2022.3223576 Go to original source...
    28. Rao, P. M., Jangirala, S., Pedada, S., Das A. K., Park, Y. (2023). Blockchain Integration for IoT-Enabled V2X Communications: A Comprehensive Survey, Security Issues and Challenges. IEEE Access, 11, 54476-54494. https://doi.org/10.1109/ACCESS.2023.3281844 Go to original source...
    29. Samuel, O., Omojo, A.B., Onuja, A.M., Sunday, Y., Tiwari, P., Gupta, D., Hafeez. G., Yahaya, A.S., Fatoba, O.J., & Shamshirband, S. (2023). IoMT: A COVID-19 Healthcare System Driven by Federated Learning and Blockchain. IEEE Journal of Biomed Health Information, 27(2), 823-834. https://doi.org/10.1109/JBHI.2022.3143576 Go to original source...
    30. Saraswat, B. K., Saxena, A., & Vashist, P. C. (2024). Machine learning for effective EHR management in blockchain-cloud integration. Journal of Autonomous Intelligence, 7(4), 1-15. https://doi.org/10.32629/jai.v7i4.1274 Go to original source...
    31. Tandon, R., Verma, A., & Gupta, P. (2024). D-BLAC: A dual blockchain-based decentralized architecture for authentication and communication in VANET. Expert Systems With Applications, 237, 121461. https://doi.org/10.1016/j.eswa.2023.121461 Go to original source...
    32. Vidal, F. R., Gouveia, F., & Soares, C. (2022). Analysis of Revocation Mechanisms for Blockchain Applications and a Proposed Model Based in Self-Sovereign Identity. Journal of Information Technology Management, 14, 192-210. https://doi.org/10.22059/jitm.2022.87848 Go to original source...
    33. Wang, Z., Chen, Q., & Liu, L. (2023). Permissioned Blockchain-Based Secure and Privacy-Preserving Data Sharing Protocol. IEEE Internet of Things Journal, 10(12), 10698-10707. https://doi.org/10.1109/jiot.2023.3242959 Go to original source...
    34. Xie, H., Zheng, J., He, T., Wei, S., Shan, C., & Hu, C. (2023). B-UAVM: A Blockchain-Supported Secure Multi-UAV Task Management Scheme. IEEE Internet of Things Journal, 10(24), 21240-21253. https://doi.org/10.1109/JIOT.2023.3279923 Go to original source...
    35. Xu, Y., Xu, G., Liu, Y., Liu, Y., & Shen, M. (2024). A survey of the fusion of traditional data security technology and blockchain. Expert Systems With Applications, 252, 124151. https://doi.org/10.1016/j.eswa.2024.124151 Go to original source...
    36. Zhang, J., Jiang, Y., Cui, J., He, D., Bolodurina, I. & Zhong, H. (2024). DBCPA: Dual Blockchain-Assisted Conditional Privacy-Preserving Authentication Framework and Protocol for Vehicular Ad Hoc Networks. IEEE Transactions on Mobile Computing, 23(2), 1127-1141. https://doi.org/10.1109/TMC.2022.3230853 Go to original source...
    37. Zhang, Y., Ma, Z., Luo S., & Duan, P. (2024). Dynamic Trust-Based Redactable Blockchain Supporting Update and Traceability. IEEE Transactions on Information Forensics and Security, 19, 821-834. https://doi.org/10.1109/TIFS.2023.3326379 Go to original source...
    38. Zhou, Z., Wan, Y., Cui, Q., Yu, K., Mumtaz, S., Yang, C., & Guizani, M. (2024). Blockchain-Based Secure and Efficient Secret Image Sharing With Outsourcing Computation in Wireless Networks. IEEE Transactions on Wireless Communications, 23(1), 423-435. https://doi.org/10.1109/TWC.2023.3278108 Go to original source...
    39. Zukaib, U., Cui, X., Hassan, M., Harris, S., Hadi, H. J., & Zheng, C. (2023). Blockchain and Machine Learning in EHR Security: A Systematic Review. IEEE Access, 11, 130230-130256. https://doi.org/10.1109/ACCESS.2023.3333229 Go to original source...

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