Automated API Performance Testing and Anomaly Detection Using Machine Learning in RESTful Architectures
Main Article Content
Abstract
As modern applications increasingly rely on RESTful APIs for communication, ensuring their performance and reliability becomes crucial for user satisfaction and system stability. Traditional API testing approaches are often manual, time-consuming, and limited in their ability to detect subtle performance degradation and anomalies. This paper presents an automated framework for API performance testing and anomaly detection using machine learning in RESTful architectures. The proposed system collects real-time performance metrics from API calls, including response time, throughput, and error rates, and applies machine learning models to identify patterns and detect deviations indicative of performance issues or anomalies. By leveraging supervised and unsupervised learning techniques, the framework is capable of dynamically adapting to changing system behaviors, predicting potential failures, and providing actionable insights for optimization. A case study demonstrates the framework’s effectiveness in a live environment, showcasing improvements in detection accuracy and a significant reduction in manual testing efforts. This solution not only enhances API reliability but also provides proactive performance management, ensuring seamless user experiences in production systems.
Article Details
References
Bahnsen, A. C., Aouada, D., Stojanovic, A., & Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. In Proceedings of the 2016 International Conference on Machine Learning and Applications (pp. 1129–1135).
Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, 448–455.
Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P. E., He-Guelton, L., & Caelen, O. (2018). Sequence classification for credit-card fraud detection. Expert Systems with Applications, 100, 234–245.
Chen, C., & Chen, M. (2020). A hybrid deep learning model for detecting financial fraud. In Proceedings of the IEEE Symposium on Computers and Communications (pp. 345–350).
Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., & Adams, N. M. (2009). Transaction aggregation as a strategy for credit card fraud detection. Data Mining and Knowledge Discovery, 18(1), 30–55.
Van Vlasselaer, V., Eliassi-Rad, T., Akoglu, L., Snoeck, M., & Baesens, B. (2017). GOTCHA! Network-based fraud detection for social security fraud. Management Science, 63(9), 3090–3110.
Pozzolo, A. D., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2018). Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3784–3797.
Carcillo, F., Dal Pozzolo, A., Le Borgne, Y. A., Caelen, O., Mazzer, Y., & Bontempi, G. (2019). Scarff: A scalable framework for streaming credit card fraud detection with Spark. Information Fusion, 41, 182–194.
Jain, J. Leveraging Advanced AI and Cloud Computing for Scalable Innovations in Fintech Systems, 2022.
Jain, J., Khunger, A., Agarwal, G., Tanikonda, A., & Modake, R. (2021). Optimizing Payment Gateways in Fintech Using AI-Augmented OCR and Intelligent Workflow. Authorea Preprints.
Jain, J., Modake, R., Khunger, A., & dnyandev Jagdale, A. CLOUD-NATIVE SECURITY FRAMEWORK: USING MACHINE LEARNING TO IMPLEMENT SELECTIVE MFA IN MODERN BANKING PLATFORMS , 2019.