Machine Learning–Enhanced Cloud Cyber Defense for Financial Systems: Intelligent Caching, Automated CI/CD Security, and Risk Classification
DOI:
https://doi.org/10.15662/IJEETR.2022.0401003Keywords:
cloud security, financial systems, machine learning, risk classification, CI/CD security, intelligent caching, anomaly detection, model explainability, DevSecOpsAbstract
Financial systems increasingly rely on cloud-native infrastructures, exposing them to sophisticated cyberattacks that exploit API vulnerabilities, high-volume transaction flows, and distributed network dependencies. Traditional security controls are insufficient for detecting complex threat patterns while simultaneously supporting the stringent performance demands of financial applications. This paper proposes a Machine Learning–Enhanced Cloud Cyber Defense Framework that unifies intelligent caching, automated CI/CD security gates, and multivariate risk classification to enable real-time protection of financial systems. The architecture integrates distributed microservices, behavioral analytics, anomaly detection models, and automated DevSecOps pipelines to reduce risk exposure and accelerate remediation.
The proposed framework employs hybrid machine learning models—including gradient boosting, LSTM anomaly detection, and multivariate risk classifiers—to identify malicious traffic, unauthorized access patterns, and fraud indicators across high-speed financial networks. Intelligent caching mechanisms are introduced to minimize latency, optimize model inference time, and ensure resilience against DDoS-like traffic spikes. In parallel, CI/CD pipelines are augmented with automated vulnerability scanning, policy enforcement, and real-time security scoring to ensure that only compliant services progress through deployment stages.
Experiments using large-scale financial logs, synthetic attack datasets, and credit risk corpora demonstrate that the framework improves threat detection accuracy by up to 94%, reduces API response latency by 38% through cache optimization, and enhances CI/CD security efficiency by 41%. Additionally, multivariate risk classification provides real-time fraud probability scores, strengthening financial decision-making under high-load conditions. Results confirm that integrating advanced machine learning with cloud-native DevSecOps automation significantly improves the security posture, performance, and operational resilience of modern financial systems.
References
1. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
2. Selvi, R., Saravan Kumar, S., & Suresh, A. (2014). An intelligent intrusion detection system using average manhattan distance-based decision tree. In Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1 (pp. 205-212). New Delhi: Springer India.
3. 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.
4. 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.
5. Sasidevi, J., Sugumar, R., & Priya, P. S. (2017). A Cost-Effective Privacy Preserving Using Anonymization Based Hybrid Bat Algorithm With Simulated Annealing Approach For Intermediate Data Sets Over Cloud Computing. International Journal of Computational Research and Development, 2(2), 173-181.
6. 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.
7. 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.
8. Rahman, M., Arif, M. H., Alim, M. A., Rahman, M. R., & Hossen, M. S. (2021). Quantum Machine Learning Integration: A Novel Approach to Business and Economic Data Analysis.
9. 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.
10. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 15:1–15:58.
11. Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. IEEE Symposium on Security and Privacy.
12. Friedman, J., Hastie, T., & Tibshirani, R. (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
13. Konidena, B. K., Bairi, A. R., & Pichaimani, T. (2021). Reinforcement Learning-Driven Adaptive Test Case Generation in Agile Development. American Journal of Data Science and Artificial Intelligence Innovations, 1, 241-273.
14. Kalyanasundaram, P. D., Kotapati, V. B. R., & Ratnala, A. K. (2021). NLP and Data Mining Approaches for Predictive Product Safety Compliance. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 1, 56-92.
15. Sabin Begum, R., & Sugumar, R. (2019). Novel entropy-based approach for cost-effective privacy preservation of intermediate datasets in cloud. Cluster Computing, 22(Suppl 4), 9581-9588.
16. 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).
17. 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
18. Kreps, J., Narkhede, N., & Rao, J. (2011). Kafka: a distributed messaging system for log processing. NetDB.
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. 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.
21. Provos, N., & Honeyman, P. (2003). Detecting and Tracing Network Intrusions Using Honeypots — early operational techniques informing modern detection.
