IAI-Assisted API Traffic Management and Anomaly Detection in High-Scale Commerce Platforms
Author(s): Viplove Goswami
Publication #: 2603031
Date of Publication: 11.10.2025
Country: United States
Pages: 1-8
Published In: Volume 11 Issue 5 October-2025
DOI: https://doi.org/10.62970/IJIRCT.v11.i5.2603031
Abstract
The quick development of high-scale commerce platforms makes us change from a fundamental to a more sophisticated way of traffic management. As these platforms evolve their architectures towards decentralized microservices, Application Programming Interfaces (APIs) are rapidly becoming the primary data delivery vehicles, which makes them prime targets for advanced automation and volumetric assault. This study investigates the infusion of Intelligent Artificial Intelligence (IAI) into the API management lifecycle to overcome the limitations of static thresholding capabilities and manual intervention. IAI-assisted frameworks utilize advanced machine learning models, Long Short-Term Memory (LSTM) networks for predictive scaling, and Random Forest ensembles for multi-layer anomaly detection to improve resilience of systems in a proactive manner. We look into context-aware monitoring systems that use dynamic knowledge graphs to correlate application-layer dependencies with infrastructure-layer resource constraints. Moreover, the study explores how service meshes and sidecar proxies can enable low latency edge inference using WebAssembly. By analyzing what industries do during the peak traffic event like a flash sale, this paper proves that IAI-driven optimization can help reduce infrastructure costs and enhance service availability. The results highlight the need for hybrid security orchestration, which combines the interpretability of deterministic logic with the adaptive analytic power of IAI to secure the future of global digital commerce.
Keywords: API Management, Intelligent Artificial Intelligence, Anomaly Detection, E-commerce Scalability, Predictive Auto-scaling, Bot Mitigation, Microservices, Zero-Trust Architecture.
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