Graph-based anomaly detection using regression on HTTP

Han Wu; Zhupeng Jiang; Fengyu He

doi:10.1117/12.2653726

8 December 2022 Graph-based anomaly detection using regression on HTTP

Han Wu, Zhupeng Jiang, Fengyu He

Proceedings Volume 12474, Second International Symposium on Computer Technology and Information Science (ISCTIS 2022); 1247406 (2022) https://doi.org/10.1117/12.2653726
Event: Second International Symposium on Computer Technology and Information Science (ISCTIS 2022), 2022, Guilin, China

Abstract

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.

Citation Download Citation

Han Wu, Zhupeng Jiang, and Fengyu He "Graph-based anomaly detection using regression on HTTP", Proc. SPIE 12474, Second International Symposium on Computer Technology and Information Science (ISCTIS 2022), 1247406 (8 December 2022); https://doi.org/10.1117/12.2653726

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