API Security Based on Automatic OpenAPI Mapping

TL;DR

This paper introduces MRG, an unsupervised, automated method for modeling, securing, and detecting anomalies in REST APIs by reconstructing API structure from real traffic without prior labels.

Contribution

It presents MRG, a novel graph-based approach that automatically infers API schemas, detects anomalies, and outperforms existing methods in speed and accuracy.

Findings

MRG achieves up to 11.4% higher recall than state-of-the-art.

It provides over 20 times faster inference.

MRG attains 100% precision on API-layer attack detection.

Abstract

This 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.