Graph-based anomaly detection using regression on HTTP
Han Wu, Zhupeng Jiang, Fengyu He · 2022
Detecting anomalies in HTTP request data is a vital security task. With big data becoming ubiquitous, techniques for structured graph data have been focused on recent years. As nodes in graphs have long-distance correlations, detecting anomaly in plain structured graph data is practical. This paper proposes a node-level feature-based regression detection method. Given a graph generated from a snapshot of HTTP request data collected by API gateway and considering clustering coefficient and empirical inspired rules, construct a regression model to dig out substantially deviate nodes. Extensive experimental studies on a real-world request dataset demonstrate that it performs relatively prominent and favorably to HBOS (a concurrent density-based method) and iForest (a linear time complexity model-based method with a low memory requirement) in terms of ROC-AUC and processing time.