Cloud-Enabled ERP Security Framework for Online Automated Cyber Defense with Oracle-Based Real-Time Analytics and Embedded Digital Forensics
DOI:
https://doi.org/10.15662/IJARCST.2024.0706005Keywords:
Cloud-enabled ERP, Cyber defense, Oracle analytics, Real-time security, Digital forensics, Automated threat detection, Cloud security, Embedded systems, Data integrity, Incident responseAbstract
The rapid expansion of Enterprise Resource Planning (ERP) systems across cloud environments has amplified the need for advanced cybersecurity mechanisms capable of real-time detection, response, and forensic analysis. This paper presents a Cloud-Enabled ERP Security Framework designed to deliver online automated cyber defense integrated with Oracle-based real-time analytics and embedded digital forensics. The proposed framework leverages intelligent monitoring, anomaly detection, and predictive threat modeling to proactively identify and neutralize cyber threats within ERP ecosystems. Through the incorporation of cloud computing, machine learning-driven analytics, and forensic automation, the system ensures continuous protection, data integrity, and regulatory compliance. The embedded digital forensics module enables traceability, incident reconstruction, and evidence preservation, facilitating swift investigation and mitigation. Experimental evaluations demonstrate that this framework significantly enhances ERP resilience, minimizes downtime, and supports adaptive threat response in dynamic enterprise cloud environments.
References
1. 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.
2. R. Sugumar, A. Rengarajan and C. Jayakumar, Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining, Middle-East Journal of Scientific Research 23 (3): 405-412, 2015.
3. Nallamothu, T. K. (2024). Real-Time Location Insights: Leveraging Bright Diagnostics for Superior User Engagement. International Journal of Technology, Management and Humanities, 10(01), 13-23.
4. Shekhar, P. C. (2023). From Traditional to Transformational: Leveraging Digital Twins for Advanced Testing in Life Insurance.
5. Kindervag, J. (2010). No more chewy centers: Introducing the zero trust model of information security. Forrester Research.
6. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2020). Explain ability and interpretability in machine learning models. Journal of Computer Science Applications and Information Technology, 5(1), 1-7.
7. Rose, S., Borchert, O., Mitchell, S., & Connelly, S. (2020). Zero Trust Architecture (NIST Special Publication 800-207). National Institute of Standards and Technology.
8. CISA. (2023). Zero Trust Maturity Model, Version 2.0. Cybersecurity & Infrastructure Security Agency.
9. Oracle Corporation. (2019). Secure critical data with Oracle Data Safe (White paper / technical report).
10. Shaffi, S. M. (2020). Comprehensive digital forensics and risk mitigation strategy for modern enterprises. International Journal of Science and Research (IJSR), 9(12), 8. https://doi.org/10.21275/sr201211165829
11. Oracle Corporation. (2023). Cybersecurity guidance and best practices for Oracle Cloud (Oracle white paper).
12. SANS Institute. (2019). ERP security: Understanding and mitigating risks (white paper).
13. Pimpale, S. Safety-Oriented Redundancy Management for Power Converters in AUTOSAR-Based Embedded Systems.
14. ISACA. (2021). ERP security and controls (ISACA Professional Practices).
15. Gandhi, S. T. (2023). RAG-Driven Cybersecurity Intelligence: Leveraging Semantic Search for Improved Threat Detection. International Journal of Research and Applied Innovations, 6(3), 8889-8897.
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. Begum, R.S, Sugumar, R., Conditional entropy with swarm optimization approach for privacy preservation of datasets in cloud [J]. Indian Journal of Science and Technology 9(28), 2016. https://doi.org/10.17485/ijst/2016/v9i28/93817’
19. 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.
20. Srinivas Chippagiri, Savan Kumar, Sumit Kumar, Scalable Task Scheduling in Cloud Computing Environments Using Swarm Intelligence-Based Optimization Algorithms‖, Journal of Artificial Intelligence and Big Data (jaibd), 1(1),1-10,2016.
21. Zwilling, M., Lesjak, D., & Kovačič, A. (2020). Cyber security threats and vulnerabilities in ERP systems. Procedia Computer Science, 176, 2242–2250.
22. Bakumenko, A., & Aivazian, V. (2022). Detecting anomalies in financial data using machine learning. Systems, 10(5), 130.
23. Peng, G., Xiao, X., Li, D., et al. (2018). SAQL: A stream-based query system for real-time abnormal system behavior detection. arXiv preprint.
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. SEI / Carnegie Mellon. (2022). Deploying a Zero Trust Architecture: Practical guidance and implementation considerations (technical report).
