A Cloud-Native AI Architecture for Financial Network Protection: Multivariate Threat Pattern Analytics with DevSecOps and Big Data–Driven ERP Security
DOI:
https://doi.org/10.15662/IJARCST.2022.0502005Keywords:
Cloud-native AI, Financial network protection, Multivariate threat analysis, DevSecOps, ERP security, Big data analytics, Threat pattern recognition, Cybersecurity automation, Predictive threat intelligence, Real-time security monitoring, Scalable cloud architecture, Enterprise risk managementAbstract
The increasing complexity of financial networks, coupled with sophisticated cyber threats, necessitates advanced, AI-driven cloud security solutions. This paper presents a cloud-native AI architecture designed for financial network protection, integrating multivariate threat pattern analytics, DevSecOps-driven automation, and big data–enabled ERP security. The framework leverages machine learning models to identify complex, multivariate threat signatures across distributed financial network topologies, enabling early detection of anomalous activities and potential breaches. DevSecOps integration ensures continuous monitoring, automated compliance enforcement, and rapid incident response within enterprise ERP systems, while big data pipelines provide scalable processing for high-velocity transactional and operational datasets. The architecture supports both real-time and predictive threat intelligence, enhancing situational awareness and risk mitigation strategies. Experimental evaluation demonstrates that the proposed system significantly improves threat detection accuracy, reduces response latency, and strengthens overall ERP security posture compared to traditional approaches. This framework provides a unified, AI-augmented, and cloud-native solution to secure financial networks, combining predictive analytics, automated security operations, and scalable data processing for enterprise-grade protection.References
1. Denning, D. E. (1987). An intrusion-detection model. IEEE Transactions on Software Engineering, SE-13(2), 222–232. (Department of Computer Science CSU)
2. Balasubramanian, V., & Rajendran, S. (2019). Rough set theory-based feature selection and FGA-NN classifier for medical data classification. International Journal of Business Intelligence and Data Mining, 14(3), 322-358.
3. Adari, V. K. (2021). Building trust in AI-first banking: Ethical models, explainability, and responsible governance. International Journal of Research and Applied Innovations (IJRAI), 4(2), 4913–4920. https://doi.org/10.15662/IJRAI.2021.0402004
4. Sudha, N., Kumar, S. S., Rengarajan, A., & Rao, K. B. (2021). Scrum Based Scaling Using Agile Method to Test Software Projects Using Artificial Neural Networks for Block Chain. Annals of the Romanian Society for Cell Biology, 25(4), 3711-3727.
5. Ravipudi, S., Thangavelu, K., & Ramalingam, S. (2021). Automating Enterprise Security: Integrating DevSecOps into CI/CD Pipelines. American Journal of Data Science and Artificial Intelligence Innovations, 1, 31-68.
6. Hardial Singh, “The Role of Multi-Factor Authentication and Encryption in Securing Data Access of Cloud Resources in a Multitenant Environment”, THE RESEARCH JOURNAL (TRJ), VOL. 4 ISSUE 4-5 JULY-OCT 2018.
7. Pichaimani, T., Inampudi, R. K., & Ratnala, A. K. (2021). Generative AI for Optimizing Enterprise Search: Leveraging Deep Learning Models to Automate Knowledge Discovery and Employee Onboarding Processes. Journal of Artificial Intelligence Research, 1(2), 109-148.
8. Vijayaboopathy, V., & Ponnoju, S. C. (2021). Optimizing Client Interaction via Angular-Based A/B Testing: A Novel Approach with Adobe Target Integration. Essex Journal of AI Ethics and Responsible Innovation, 1, 151-186.
9. Sivaraju, P. S. (2021). 10x Faster Real-World Results from Flash Storage Implementation (Or) Accelerating IO Performance A Comprehensive Guide to Migrating From HDD to Flash Storage. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(5), 5575-5587.
10. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.
11. Tavallaee, M., & Ghorbani, A. A. (2009). NSL-KDD dataset: Improved dataset for IDS evaluation. (paper introducing NSL-KDD as an improved evaluation dataset). (ee.torontomu.ca)
12. Mahoney, M. V., & Chan, P. K. (2003). An analysis of the 1999 DARPA/Lincoln Laboratory evaluation data for network anomaly detection. Springer/RAID Workshop. (SpringerLink)
13. U
14. lvila, J. W., & Gaffney, J. E., Jr. (2003). Evaluation of intrusion detection systems. Journal of Research of the National Institute of Standards and Technology, 108(6), 453–473. (PMC)
15. Camacho, J., et al. (2019). Multivariate big data analysis for intrusion detection. Computers & Security (or similar venue). (ScienceDirect)
16. Moustafa, N., & Slay, J. (2015). UNSW-NB15: A comprehensive modern network intrusion dataset for evaluation. Military Communications and Information Systems conference (or IEEE TPC). (dataset paper widely used for modern IDS research).
17. Khreich, W., & others. (2019). Survey and taxonomy of ML approaches for intrusion detection. (survey summarizing modern ML approaches). (SpringerLink)
18. Kumar, R., & others. (2020). A conceptual model for automated DevSecOps using open source. Computers & Security (conceptual ADOC model). (ScienceDirect)
19. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.
20. Navandar, P. (2021). Fortifying cybersecurity in Healthcare ERP systems: unveiling challenges, proposing solutions, and envisioning future perspectives. Int J Sci Res, 10(5), 1322-1325.
21. Althati, C., Krothapalli, B., Konidena, B. K., & Konidena, B. K. (2021). Machine learning solutions for data migration to cloud: Addressing complexity, security, and performance. Australian Journal of Machine Learning Research & Applications, 1(2), 38-79.
22. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2020). Artificial intelligence using TOPSIS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 3(6), 4305-4311.
23. Sugumar, R. (2018). Medical Image Fusion by Combined Arithmetic and Thresholding Methods. EDITORS OF SPECIAL ISSUE JOURNAL, 17.
24. Girdhar, P., Virmani, D., & Saravana Kumar, S. (2019). A hybrid fuzzy framework for face detection and recognition using behavioral traits. Journal of Statistics and Management Systems, 22(2), 271-287.
25. Anuj Arora, “Analyzing Best Practices and Strategies for Encrypting Data at Rest (Stored) and Data in Transit (Transmitted) in Cloud Environments”, “INTERNATIONAL JOURNAL OF RESEARCH IN ELECTRONICS AND COMPUTER ENGINEERING”, VOL. 6 ISSUE 4 ( OCTOBER- DECEMBER 2018).
26. Khreisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019). Techniques, datasets and challenges in IDS research (comprehensive survey consolidating dataset issues and method gaps). (SpringerLink)
