AI-DRIVEN SECURITY INTELLIGENCE: TRANSFORMING JAVA ENTERPRISE OBSERVABILITY INTO PROACTIVE CYBER THREAT DETECTION
DOI:
https://doi.org/10.34218/IJCET_15_01_015Keywords:
Anomaly Detection, Cybersecurity, Java Enterprise Applications, Machine Learning, Cloud Observability, AWS CloudWatch, Threat IntelligenceAbstract
This paper proposes an AI-enhanced anomaly detection pipeline for Java enterprise applications using Spring, Log4j, and AWS CloudWatch. The methodology utilizes historical logs from applications deployed on ECS and Lambda to train unsupervised ML models (e.g., Isolation Forests) to detect operational and security anomalies. Real-time inference is served via serverless endpoints, with threat scores visualized in CloudWatch Dashboards. Integration with AWS KMS and Secrets Manager enforces secure data handling. The study includes detection of synthetic attacks in a simulated financial workload, demonstrating how full-stack observability evolves into proactive cybersecurity.
References
Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19-31.
Burns, B., & Beda, J. (2019). Kubernetes: Up and Running. O'Reilly Media.
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1-58.
Chen, L., Wang, S., & Liu, H. (2018). Static analysis for Java application security monitoring. IEEE Transactions on Software Engineering, 44(9), 847-861.
Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., & Vázquez, E. (2009). Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security,
(1-2), 18-28.
Liu, F., Ting, K. M., & Zhou, Z. H. (2020). Isolation-based anomaly detection. ACM Transactions on Knowledge Discovery from Data, 6(1), 1-39.
Akoglu, L., Tong, H., & Koutra, D. (2015). Graph based anomaly detection and description: a survey. Data Mining and Knowledge Discovery, 29(3), 626-688.
Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176.
Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019). Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity, 2(1), 1-22.
Liu, H., & Shah, S. (2007). Applying machine learning algorithms to web anomaly detection. International Journal of Network Security, 5(3), 321-332.
Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2021). Deep learning for anomaly detection: A review. ACM Computing Surveys, 54(2), 1-38.
Ring, M., Wunderlich, S., Scheuring, D., Landes, D., & Hotho, A. (2019). A survey of network-based intrusion detection data sets. Computers & Security, 86, 147-167.
Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020). Cybersecurity data science: an overview from machine learning perspective. Journal of Big
Data, 7(1), 1-29.
Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. Proceedings of the 4th International Conference on Information Systems Security and Privacy, 108-116.
Vasilomanolakis, E., Karuppayah, S., Mühlhäuser, M., & Fischer, M. (2015). Taxonomy and survey of collaborative intrusion detection. ACM Computing Surveys, 47(4), 1-33.
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., ... & Wang, C. (2018). Machine learning and deep learning methods for cybersecurity. IEEE Access, 6, 35365-35381.
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Copyright (c) 2024 Chandra Sekhar Oleti (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
