Deep Learning Driven Predictive Threat Detection Framework for Secure Financial and Healthcare Cloud Platforms
DOI:
https://doi.org/10.15662/IJEETR.2024.0604013Keywords:
Deep Learning, Cloud Security, Predictive Threat Detection, Financial Systems Security, Healthcare Data Protection, Cybersecurity Analytics, Artificial Intelligence, Intrusion Detection Systems, Anomaly Detection, Cloud Computing SecurityAbstract
Cloud computing has become a fundamental infrastructure for financial institutions and healthcare organizations due to its scalability, cost efficiency, and accessibility. However, the migration of sensitive financial transactions and medical records to cloud environments introduces significant cybersecurity risks, including data breaches, ransomware attacks, insider threats, and advanced persistent threats. Traditional rule-based security mechanisms are often insufficient to detect sophisticated and evolving cyberattacks in real time. This research proposes a deep learning driven predictive threat detection framework designed to enhance the security of cloud platforms used in financial and healthcare systems. The proposed framework integrates deep neural networks, anomaly detection models, and behavioral analytics to analyze large-scale cloud activity logs and network traffic patterns. By leveraging deep learning algorithms such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks, the framework can identify abnormal behaviors and predict potential threats before they cause damage. The model continuously learns from historical and real-time data to improve detection accuracy and reduce false positives. The study evaluates the framework’s performance using benchmark cybersecurity datasets and cloud environment simulations. The results demonstrate that deep learning-based predictive security systems significantly enhance proactive threat detection, improve incident response time, and strengthen data protection for sensitive financial and healthcare information in cloud infrastructures.References
1. Nguyen, H., & Chien, A. (2023). Storm-RTS: Stream processing with stable performance for multi-cloud and cloud-edge environments. In Proceedings of the IEEE 16th International Conference on Cloud Computing (CLOUD 2023). IEEE.
2. Gopinathan, V. R. (2024). Meta-Learning–Driven Intrusion Detection for Zero-Day Attack Adaptation in Cloud-Native Networks. International Journal of Humanities and Information Technology, 6(01), 19-35.
3. Madathala, H., Barmavat, B., & Thumala, S. (2023). Performance optimization of sap hana using ai-based workload predictions. International Journal of Innovative Research in Science, Engineering and Technology, 12, 15315-15326.
4. Sridevi, V., Azath, H., Vijayakumar, R., Anbuselvan, N., Amirthalingam, V., & Arunkumar, S. (2024, April). Augmented Reality Shopping and IoT-Enabled Virtual Try-On with Cloud Services for Interactive Product Displays. In 2024 10th International Conference on Communication and Signal Processing (ICCSP) (pp. 880-885). IEEE.
5. Potel, R. (2023). Artificial Intelligence in Human Capital Management: A Comprehensive Framework for Intelligent Workforce Systems. International Journal of AI, BigData, Computational and Management Studies, 4(4), 147-174.
6. Jagadeesh, S., & Soundappan, R. S. (2014). Survey on knowledge discovery in speech emotion detection. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4476–4481.
7. Devi, C., Musunuru, M. V., & Mohammed, A. S. (2023). Reinforcement-Learning Scheduler for Multi-Tenant Spark Clustersunder Privacy Constraints. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 496-527.
8. Karnam, A. (2023). SAP Beyond Uptime: Engineering Intelligent AMS with High Availability & DR through Pacemaker Automation. International Journal of Research Publications in Engineering, Technology and Management, 6(5), 9351–9361. https://doi.org/10.15662/IJRPETM.2023.0605011
9. Yashwanth, K., Adithya, N., Sivaraman, R., Janakiraman, S., & Rengarajan, A. (2021, July). Design and Development of Pipelined Computational Unit for High-Speed Processors. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.
10. Mudunuri, P. R. (2024). Scalable secrets governance models for high-sensitivity biomedical systems. International Journal of Computer Technology and Electronics Communication, 7(1), 8220-8232.
11. Rao, N. S., Shanmugapriya, G., Vinod, S., & Mallick, S. P. (2023, March). Detecting human behavior from a silhouette using convolutional neural networks. In 2023 Second International Conference on Electronics and Renewable Systems (ICEARS) (pp. 943-948). IEEE.
12. Bheemisetty, N. (2024). From Fragmentation to Agility: Nautilus Architecture for Risk Management Modernization. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10673-10682.
13. Poornima, G., & Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.
14. Indurthy, V. S. K. (2024). Streamlining ROP Metrics and Reporting through Cloud Migration and Automation. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10703-10712.
15. Karvannan, R. (2023). Real-Time Prescription Management System Intake & Billing System. International Journal of Humanities and Information Technology, 5(02), 34-43.
16. Sumathi, R., & Umasankar, P. (2023). A hybrid approach for power flow management in smart grid connected system. IETE Journal of Research, 69(8), 5204-5218.
17. Ireddy, Ravi Kumar. (2023). API-driven interoperability framework for corporate treasury management: A financial data exchange standard implementation with secure data aggregation networks. World Journal of Advanced Research and Reviews, 19(2), 1727–1738. https://doi.org/10.30574/wjarr.2023.19.2.1609
18. Dave, B. L. (2023). Enhancing Vendor Collaboration via an Online Automated Application Platform. International Journal of Humanities and Information Technology, 5(02), 44-52.
19. Ambalakannu, M. (2024). Driving Operational Efficiency and Clinical Insights via Unified Care Management. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10693-10702.
20. Kwankajornkeat, S., & Aswakul, C. (2021). Differential private motion sensor and wasted energy in building energy management system. IEEE Access, 10, 486-501.
21. Vootla, A. (2023). Continuous Accessibility Assurance through DevSecOps-Integrated Testing Pipelines. International Journal of Research and Applied Innovations, 6(6), 9975-9984.
22. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
23. Kothokatta, L. (2020). Scalable validation and continuous verification of AI/ML systems on AWS using Python-based automation. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 3(5), 5131–5138.
24. Konda, S. K. (2024). Carbon-native DCIM architectures for AI data centers: Autonomous infrastructure control via smart grid intelligence. World Journal of Advanced Research and Reviews, 21(1), 3008–3318. https://doi.org/10.30574/wjarr.2024.21.1.0095
25. Selvi, C. P., Muneeshwari, P., Selvasheela, K., & Prasanna, D. (2023). Twitter Media Sentiment Analysis to Convert Non-Informative to Informative Using QER. Intelligent Automation & Soft Computing, 35(3).
26. Uttama Reddy Sanepalli. (2022). Adaptive Intelligence Framework for Retirement Portfolio Management: Self-Optimizing Infrastructure for Dynamic Asset Allocation and Risk Mitigation. International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), 8(6), 769-780. https://doi.org/10.32628/CSEIT22557
27. Meka, S. (2023). Building Digital Banking Foundations: Delivering End-to-End FinTech Solutions with Enterprise-Grade Reliability. International Journal of Research and Applied Innovations, 6(2), 8582-8592.
28. Dama, H. B. (2023). Designing Highly Available Multi-Cloud Database Architectures for Global Financial Services. International Journal of Research and Applied Innovations, 6(1), 8329-8336.
29. Sugumar, R. (2024). Quantum-Resilient Cryptographic Protocols for the Next-Generation Financial Cybersecurity Landscape. International Journal of Humanities and Information Technology, 6(02), 89-105.
30. Inampudi, R. K., Pichaimani, T., & Surampudi, Y. (2022). AI-enhanced fraud detection in real-time payment systems: leveraging machine learning and anomaly detection to secure digital transactions. Australian Journal of Machine Learning Research & Applications, 2(1), 483-523.
31. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
32. Barve, P. S., Vigenesh, M., Deshpande, V., Wanjari, M. B., & Patil, S. (2023, December). A Non-Linear Dimensionality Reduction Approach for Unmixing Hyper Spectral Data. In 2023 International Conference on Power Energy, Environment & Intelligent Control (PEEIC) (pp. 1718-1724). IEEE.
33. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
34. Rattihalli, G., Hogade, N., Dhakal, A., Frachtenberg, E., Hong Enriquez, R. P., Bruel, P., Mishra, A., & Milojicic, D. (2023). Fine-grained heterogeneous execution framework with energy-aware scheduling. In Proceedings of the IEEE 16th International Conference on Cloud Computing (CLOUD 2023). IEEE.
35. Kesavan, E., & Srinivasulu, S. (2024). Security challenges in smart IoT systems and their solutions. Journal of Information Technology, 14(2). https://doi.org/10.26634/jit.14.2.22000
