信息安全研究2025，Vol.11Issue(10)：917-923,7.DOI:10.12379/j.issn.2096-1057.2025.10.06
API网关流量异常检测方法及系统研究
Research on Traffic Anomaly Detection Method and System for API Gateway
江洁 1顾宁伦 1乔峤2
作者信息
- 1. 中国移动通信有限公司 北京 100032
- 2. 天津大学电气自动化与信息工程学院 天津 300072
- 折叠
摘要
Abstract
With the rise of cloud services and the widespread use of API technology,many network capabilities of operators are usually outputted and empowered through APIs.API gateways have become an important way for north-south and east-west system interconnection and data sharing.This paper proposes a method for API gateway traffic anomaly detection based deep learning.Firstly,a heterogeneous graph is constructed to comprehensively represent the gateway traffic network.Then,based on graph attention neural network,node representations in the heterogeneous graph are learned by considering both structural and temporal dimensions.We introduce graph structure refinement to compensate for sparse connections between entities in the heterogeneous graph and obtain more robust node representation learning;Finally,the meta learning algorithm is used to optimize the model and improve its generalization ability in small sample scenarios.The model can be deployed on gateway devices.The algorithm model was experimentally evaluated on the CICIDS2017 dataset,and the results showed that compared with the baseline algorithm,the detection method proposed in this paper has good performance in small sample and multi classification problems.关键词
API网关/网络流量异常检测/数据不平衡/动态异构网络/节点嵌入/元学习Key words
API gateway/network traffic anomaly detection/data imbalance/dynamic heterogeneous network/node embedding/meta learning分类
信息技术与安全科学引用本文复制引用
江洁,顾宁伦,乔峤..API网关流量异常检测方法及系统研究[J].信息安全研究,2025,11(10):917-923,7.
