The paper introduces Map Reduce Graph (MRG), an unsupervised method that automatically models and secures HTTP REST APIs by learning their structure from real-world traffic, achieving high accuracy and speed in anomaly detection.
Abstract
More Like ThisThis paper presents Map Reduce Graph (MRG), a novel unsupervised method for modeling and securing HTTP REST APIs. MRG learns API structure from real-world traffic without prior knowledge or labels, automatically generating OpenAPI-compliant documentation by reconstructing routes, methods, and parameter formats. MRG enables real-time updates, explainable visualization, and anomaly detection, helping identify undocumented or evolving behaviors. It detects malformed requests, structural deviations, and injection attacks using graph-based validation and a deep autoencoder for payload analysis. Compared to state-of-the-art methods like HRAL and FT-ANN, MRG achieves up to 11.4% higher recall, over 20 times faster inference, and perfect precision (100%) on multiple API-layer attacks. Designed for dynamic microservice environments, MRG operates in three phases - training, updating, and detection - and integrates smoothly with observability and security tools. This work contributes a fully automated, efficient pipeline for real-time API visibility, schema inference, and anomaly detection without manual tuning or labeled data.