App. Res. of Comp. Accepted Paper
Vol. 44, 2027 No. 1

Anomaly network node detection approach based on graphsage and edge attention mechanism

Li Xin1,2
Ye Xiaoming1,2
Ou Lujin1,2
Kong Tenglong1,2
1. School of Cybersecurity, Xin Gu Industrial College, Chengdu University of Information Technology, Chengdu 610225, China
2. Sichuan Provincial Key Laboratory of Cyberspace Security, Chengdu 610225, China
DOI: 10.19734/j.issn.1001-3695.2026.04.0132

Abstract

Existing network behavior anomaly detection methods have limitations in multi-view topology modeling, key traffic edge perception, and collaborative representation of node topology and edge traffic behavior. These limitations reduce detection accuracy for complex and stealthy attacks. To address these limitations, this paper proposed GEA-AND, an anomalous network node detection method based on GraphSAGE and an edge attention mechanism. The method used time windows to construct dual-view network communication graphs. It extracted node features from both views to enhance structural representation. It dynamically weighted traffic edge features through an edge attention mechanism to improve the representation of node communication behavior. The method further constructed a traffic-edge-aware GraphSAGE that aggregated traffic edge features and neighbor node embeddings. It generated low-dimensional node representations that captured both topological structures and communication behavior patterns, thereby supporting accurate identification of anomalous network nodes. Experimental results show that the method achieved F1 scores of 99.71%, 99.13%, and 99.99% on the CIC-IDS-2017, CIC-UNSW-NB15, and NF-CIC-IDS-2018 datasets, respectively. The method outperforms RE-GCN, XG-BoT, GraphFedAI, GraphSAGE, and other methods. GEA-AND effectively integrates node topology and traffic-edge information. It provides a new technical approach for anomalous network node detection in complex network environments.

Foundation Support

国家社会科学基金项目(23BSH061)
国家自然科学基金项目(62272066)
四川省科技支撑计划项目(23YFG0292)
成都信息工程大学科技创新能力提升计划项目(KYTD202512)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.04.0132
Publish at: Application Research of Computers Accepted Paper, Vol. 44, 2027 No. 1

Publish History

[2026-08-27] Accepted Paper

Cite This Article

李鑫, 叶晓鸣, 欧鲁金, 等. 基于GraphSAGE和边注意力机制的异常网络节点检测方法 [J]. 计算机应用研究, 2027, 44 (1). (2026-09-14). https://doi.org/10.19734/j.issn.1001-3695.2026.04.0132. (Li Xin, Ye Xiaoming, Ou Lujin, et al. Anomaly network node detection approach based on graphsage and edge attention mechanism [J]. Application Research of Computers, 2027, 44 (1). (2026-09-14). https://doi.org/10.19734/j.issn.1001-3695.2026.04.0132. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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