Hierarchical Deep Learning for Robust Cybersecurity in Multi-Cloud Healthcare Infrastructures
Received: 5 September 2024 | Revised: 11 October 2024 | Accepted: 4 December 2024 | Online: 2 February 2025
Corresponding author: Tariq Emad Ali
Abstract
Patient safety is in danger because healthcare networks are more susceptible to cyberattacks as they become more intricate and linked. By altering data transmitted between various system components, malicious actors can hack into these networks. As cloud, edge, and IoT technologies become more widely used in contemporary healthcare systems, this difficulty is predicted to increase. This study presents a Combined Hybrid Deep Learning Framework with Layer Reuse for Cybersecurity (CHDLCY) to address this issue. This system is built to detect malicious actions that modify the metadata or payload of data flows across IoT gateways, edge, and core clouds quickly and precisely. The CHDLCY's is a unique design demanding less training time, while bigger models at the core cloud profit from a cutting-edge layer-merging method. The core cloud model is partially pre-trained by reusing layers from trained edge cloud models, which drastically reduces the number of training epochs required from 35 to 40 to just 6 to 8. Thorough tests demonstrated that CHDLCY not only accelerates the training phase but also achieves remarkable accuracy rates, ranging from 98% to 100%, in identifying cyber threats. The proposed approach offers a significant improvement over previous models in terms of training efficiency and generalizability to new datasets.
Keywords:
cloud networks, edge clouds, NFV, healthcare, DNN, autoencoders, cybersecurityDownloads
References
"National health expenditure data: NHE fact sheet," Center for Medicare and Medicaid Services. http://www.cms.hhs.gov/NationalHealthExpendData/25_NHE_Fact_Sheet.asp.
F. I. Ali, T. E. Ali, and A. H. Hamad, "Telemedicine Framework in COVID-19 Pandemic," in 2022 International Conference on Engineering and Emerging Technologies (ICEET), Kuala Lumpur, Malaysia, Oct. 2022, pp. 1–8.
R. Zorgati, H. Hassen, and K. A. Alsulbi, "The Deployment of E-Learning Application as a Web Service in a Cloud Broker Architecture," in Advanced Information Networking and Applications, Cham, 2024, pp. 1–12.
L. Gupta, T. Salman, A. Ghubaish, D. Unal, A. K. Al-Ali, and R. Jain, "Cybersecurity of multi-cloud healthcare systems: A hierarchical deep learning approach," Applied Soft Computing, vol. 118, Mar. 2022, Art. no. 108439.
F. I. Ali, T. E. Ali, and Z. T. Al_dahan, "Private Backend Server Software-Based Telehealthcare Tracking and Monitoring System," International Journal of Online and Biomedical Engineering (iJOE), vol. 19, no. 01, pp. 119–134, Jan. 2023.
G. Alotibi, "A Cybersecurity Awareness Model for the Protection of Saudi Students from Social Media Attacks," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13787–13795, Apr. 2024.
C. Kowalkowski, J. Wirtz, and M. Ehret, "Digital service innovation in B2B markets," Journal of Service Management, vol. 35, no. 2, pp. 280–305, Dec. 2023.
"2024 Data Breach Investigations Report," Verizon Business. https://www.verizon.com/business/resources/reports/dbir/.
“Cyber Claims Study 2020 Report,” NetDiligence, Nov. 11, 2020. https://netdiligence.com/cyber-claims-study-2020-report/.
S. J. Choi and M. E. Johnson, "Do Hospital Data Breaches Reduce Patient Care Quality?" arXiv, Apr. 03, 2019.
T. E. Ali, Y.-W. Chong, and S. Manickam, "Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN," Applied Sciences, vol. 13, no. 5, Jan. 2023, Art. no. 3033.
T. E. Ali, Y. W. Chong, and S. Manickam, "Machine Learning Techniques to Detect a DDoS Attack in SDN: A Systematic Review," Applied Sciences, vol. 13, no. 5, Jan. 2023, Art. no. 3183.
T. Emad Ali, F. Imad Ali, A. Hussein Morad, and M. A Abdala, "Diabetic Patient Real-Time Monitoring System Using Machine Learning," International Journal of Computing and Digital Systems, vol. 16, no. 1, pp. 189–199, 2024.
A. B. Nassif, M. A. Talib, Q. Nasir, H. Albadani, and F. M. Dakalbab, "Machine Learning for Cloud Security: A Systematic Review," IEEE Access, vol. 9, pp. 20717–20735, 2021.
C. E. L. Asry, I. Benchaji, S. Douzi, and B. E. L. Ouahidi, "A robust intrusion detection system based on a shallow learning model and feature extraction techniques," PLOS ONE, vol. 19, no. 1, 2024, Art. no. e0295801.
V. Hayyolalam, M. Aloqaily, Ö. Özkasap, and M. Guizani, "Edge Intelligence for Empowering IoT-Based Healthcare Systems," IEEE Wireless Communications, vol. 28, no. 3, pp. 6–14, Jun. 2021.
H. Elayan, M. Aloqaily, and M. Guizani, "Digital Twin for Intelligent Context-Aware IoT Healthcare Systems," IEEE Internet of Things Journal, vol. 8, no. 23, pp. 16749–16757, Sep. 2021.
F. Zaman, M. Aloqaily, F. Sallabi, K. Shuaib, and J. B. Othman, "Application of Graph Theory in IoT for Optimization of Connected Healthcare System," in GLOBECOM 2020 - 2020 IEEE Global Communications Conference, Taipei, Taiwan, Dec. 2020, pp. 1–6.
M. Al-Khafajiy et al., "Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model," ACM Transactions on Internet Technology, vol. 21, no. 3, pp. 1–23, Aug. 2021.
P. Kumar, R. Kumar, G. P. Gupta, R. Tripathi, A. Jolfaei, and A. K. M. Najmul Islam, "A blockchain-orchestrated deep learning approach for secure data transmission in IoT-enabled healthcare system," Journal of Parallel and Distributed Computing, vol. 172, pp. 69–83, Feb. 2023.
S. Hizal, U. Cavusoglu, and D. Akgun, "A new Deep Learning Based Intrusion Detection System for Cloud Security," in 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara, Turkey, Jun. 2021, pp. 1–4.
L. Chen, X. Kuang, A. Xu, S. Suo, and Y. Yang, "A Novel Network Intrusion Detection System Based on CNN," in 2020 Eighth International Conference on Advanced Cloud and Big Data (CBD), Taiyuan, China, Dec. 2020, pp. 243–247.
T. E. Ali, F. I. Ali, N. Pataki, and A. D. Zoltán, "Exploring Attribute-Based Facial Synthesis with Generative Adversarial Networks for Enhanced Patient Simulator Systems," in 2024 7th International Conference on Software and System Engineering (ICoSSE), Paris, France, Apr. 2024, pp. 53–60.
Y. Xun, J. Qin, and J. Liu, "Deep Learning Enhanced Driving Behavior Evaluation Based on Vehicle-Edge-Cloud Architecture," IEEE Transactions on Vehicular Technology, vol. 70, no. 6, pp. 6172–6177, Jun. 2021.
M. He, X. Wang, J. Zhou, Y. Xi, L. Jin, and X. Wang, "Deep-Feature-Based Autoencoder Network for Few-Shot Malicious Traffic Detection," Security and Communication Networks, vol. 2021, no. 1, 2021, Art. no. 6659022.
H. Elayan, M. Aloqaily, and M. Guizani, "Digital Twin for Intelligent Context-Aware IoT Healthcare Systems," IEEE Internet of Things Journal, vol. 8, no. 23, pp. 16749–16757, Sep. 2021.
S. Khasim, H. Ghosh, I. S. Rahat, K. Shaik, and M. Yesubabu, "Deciphering Microorganisms through Intelligent Image Recognition: Machine Learning and Deep Learning Approaches, Challenges, and Advancements," EAI Endorsed Transactions on Internet of Things, vol. 10, 2024.
L. Gupta, "Hierarchical Deep Learning for Cybersecurity of Critical Service Systems," in 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), London, United Kingdom, Jul. 2020, pp. 346–351.
A. Dhulfiqar, N. Pataki, and M. Tejfel, "Chatbot-Based Querying of IoT Devices in EdgeX," presented at the SQAMIA 2023: Workshop on Software Quality Analysis, Monitoring, Improvement, and Applications, Bratislava, Slovakia, Sep. 2013.
F. Eyvazov, T. E. Ali, F. I. Ali, and A. D. Zoltan, "Beyond Containers: Orchestrating Microservices with Minikube, Kubernetes, Docker, and Compose for Seamless Deployment and Scalability," in 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, Mar. 2024, pp. 1–6.
Y. Imrana, Y. Xiang, L. Ali, A. Noor, K. Sarpong, and M. A. Abdullah, "CNN-GRU-FF: a double-layer feature fusion-based network intrusion detection system using convolutional neural network and gated recurrent units," Complex & Intelligent Systems, vol. 10, no. 3, pp. 3353–3370, Jun. 2024.
S. Attar-Khorasani and R. Chalmeta, "Internet of Things Data Visualization for Business Intelligence," Big Data, vol. 12, no. 6, pp. 478–503, Dec. 2024.
J. M. E. Gray, "Codified Disparity: The Medicaid IMD Exclusion, Mental Health Parity, and Congressional Intent," Indiana Health Law Review, vol. 21, 2024, Art. no. 227.
K. Hacker, "The Burden of Chronic Disease," Mayo Clinic Proceedings: Innovations, Quality & Outcomes, vol. 8, no. 1, pp. 112–119, Feb. 2024.
C. Min, J. Yi, U. G. Acer, and F. Kawsar, "Enabling Cross-Camera Collaboration for Video Analytics on Distributed Smart Cameras." arXiv, Jan. 27, 2024.
X. Zhou, "Data Mining of the Underachievers’ Performance of E-learning and Finals in College English with SPSS and WEKA," in 2024 5th International Conference on Computer Engineering and Application (ICCEA), Hangzhou, China, Apr. 2024, pp. 767–770.
Downloads
How to Cite
License
Copyright (c) 2025 Tariq Emad Ali, Alwahab Dhulfiqar Zoltan

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.