AI-Driven ERP Cybersecurity for Fuel Cell Vehicles: Real-Time Threat Detection in Oracle and SAP Using NLP and U-Net Denoising
DOI:
https://doi.org/10.15662/IJARCST.2024.0704004Keywords:
AI-driven ERP security, fuel cell vehicles, Oracle ERP, SAP ERP, real-time threat detection, NLP, U-Net denoising, anomaly detection, cloud cybersecurity, smart manufacturing, zero-day attacks, deep learning, cyber defense automation, data integrity, automotive cybersecurityAbstract
The rapid digital transformation of automotive manufacturing, particularly in the domain of fuel cell vehicles (FCVs), has increased reliance on ERP platforms such as Oracle and SAP for integrated process management. However, this dependency exposes systems to sophisticated cyber threats that can compromise operational integrity and data confidentiality. This paper proposes an AI-driven ERP cybersecurity framework that integrates Natural Language Processing (NLP) for intelligent log analysis and U-Net-based denoising for anomaly detection in sensor and communication data. The proposed model enhances real-time threat detection through adaptive learning mechanisms, reducing false positives and improving response latency in cloud-enabled ERP environments. The system architecture leverages deep neural layers for context-aware cybersecurity analytics, ensuring the secure operation of Oracle and SAP ERP systems deployed in fuel cell vehicle ecosystems. Experimental validation demonstrates improved detection accuracy, resilience against zero-day attacks, and seamless interoperability with ERP security modules. This study provides a foundation for next-generation cyber defense automation in smart manufacturing and connected mobility sectors.
References
1. Gao, P., Xiao, X., Li, D., et al. (2018). SAQL: A stream-based query system for real-time abnormal system behavior detection. USENIX Security / ICDE Proceedings.
2. Karvannan, R. (2023). Real‑Time Prescription Management System Intake & Billing System. International Journal of Humanities and Information Technology, 5(02), 34-43.
3. Srinivas Chippagiri , Savan Kumar, Olivia R Liu Sheng,‖ Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media‖, Journal of Artificial Intelligence and Big Data(jaibd),1(1),11-20,2016.
4. R., Sugumar (2023). Real-time Migration Risk Analysis Model for Improved Immigrant Development Using Psychological Factors. Migration Letters 20 (4):33-42.
5. Grabski, S. V., Leech, S. A., & Schmidt, P. J. (2011). A review of ERP research: A future agenda for accounting information systems. Journal of Information Systems, 25(1), 37–78.
6. ISACA. (2021). ERP security and controls (ISACA Professional Practices Paper).
7. NIST. (2020). Rose, S., Borchert, O., Mitchell, S., & Connelly, S. Zero Trust Architecture (NIST Special Publication 800-207). National Institute of Standards and Technology.
8. Adari, Vijay Kumar, “Interoperability and Data Modernization: Building a Connected Banking Ecosystem,” International Journal of Computer Engineering and Technology (IJCET), vol. 15, no. 6, pp.653-662, Nov-Dec 2024. DOI:https://doi.org/10.5281/zenodo.14219429.
9. Oracle Corporation. (2019). Secure critical data with Oracle Data Safe (technical report).
10. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2023). Ethical analysis and decision-making framework for marketing communications: A weighted product model approach. Data Analytics and Artificial Intelligence, 3(5), 44–53. https://doi.org/10.46632/daai/3/5/7
11. Oracle Corporation. (2023). Cloud security and cybersecurity guidance and best practices (Oracle white paper).
12. Peng, G., Gao, P., Xiao, X., et al. (2018). Querying streaming system monitoring data for enterprise anomaly detection. ICDE / Usenix Presentation / arXiv.
13. Komarina, G. B. (2024). Transforming Enterprise Decision-Making Through SAP S/4HANA Embedded Analytics Capabilities. Journal ID, 9471, 1297.
14. Ramanathan, U.; Rajendran, S. Weighted Particle Swarm Optimization Algorithms and Power Management Strategies for Grid Hybrid Energy Systems. Eng. Proc. 2023, 59, 123. [Google Scholar] [CrossRef]
15. SANS Institute. (2019). ERP security: Understanding and mitigating risks (white paper).
16. Subramanian, G. H. (2017). Cloud ERP implementation and the impact of cloud computing on ERP. International Journal of Enterprise Information Systems, 13(4), 21–34.
17. Vaidya, S., & Seetharaman, P. (2020). Artificial intelligence applications in ERP systems. Information Systems Frontiers, 22(2), 475–491.
18. Wang, X., & Zhang, Y. (2022). Real-time fraud detection in financial systems: Techniques and systems. Journal of Financial Crime, 29(3), 874–896.
19. Pimpale, S. Comparative Analysis of Hydrogen Fuel Cell Vehicle Powertrain with Battery Electric. Hybrid, and Gasoline Vehicles.
20. Yu, J., Kim, M., Oh, H., & Yang, J. (2021). Real-time abnormal insider event detection on enterprise resource planning systems via predictive auto-regression model. IEEE Access, 9, 62276–62284.
21. Chellu, R. Integrating IBM Sterling Control Center with Google Cloud Platform for Better Monitoring and Management of Real Time File Transfers.
22. Zwilling, M., Lesjak, D., & Kovačič, A. (2020). Cyber security threats and vulnerabilities in ERP systems. Procedia Computer Science, 176, 2242–2250.
23. Bakumenko, A., & Aivazian, V. (2022). Detecting anomalies in financial data using machine learning. Systems, 10(5), 130.
24. Manda, P. (2024). Navigating the Oracle EBS 12.1. 3 to 12.2. 8 Upgrade: Key Strategies for a Smooth Transition. International Journal of Technology, Management and Humanities, 10(02), 21-26.
25. S. T. Gandhi, "Context Sensitive Image Denoising and Enhancement using U-Nets," Computer Science (MS), Computer Science (GCCIS), Rochester Institute of Technology, 2020. [Online]. Available: https://repository.rit.edu/theses/10588/
26. Komarina, G. B. (2024). Transforming Enterprise Decision-Making Through SAP S/4HANA Embedded Analytics Capabilities. Journal ID, 9471, 1297.
27. SEI / Carnegie Mellon. (2022). Deploying a Zero Trust Architecture: Practical guidance and implementation considerations (technical report).
