PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization-Reference-Cited by-同舟云学术

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PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization

Published:2024-03-04 Issue: Volume: Page:920-929
ISSN:
Container-title:Proceedings of the 17th ACM International Conference on Web Search and Data Mining
language:
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Author:

Yuan Ziqi¹^ORCID,Zhou Haoyi²^ORCID,Chen Tianyu¹^ORCID,Li Jianxin³^ORCID

Affiliation:

1. School of Computer Science and Engineering, Beihang University, Beijing, China

2. Zhongguancun Laboratory & School of Software, Beihang University, Beijing, China

3. Zhongguancun Laboratory & School of Computer Science and Engineering, Beihang University, Beijing, China

Funder

National Natural Science Foundation of China

CAAI-Huawei MindSpore Open Fund

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3616855.3635783

Reference46 articles.

1. Sambaran Bandyopadhyay, Lokesh N, Saley Vishal Vivek, and M. Narasimha Murty. 2020. Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding. In WSDM. ACM, 25--33.

2. A modified DeepWalk method for link prediction in attributed social network

3. Anomal-E: A self-supervised network intrusion detection system based on graph neural networks

4. Ting Chen, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, and Kai Zhang. 2016. Entity Embedding-Based Anomaly Detection for Heterogeneous Categorical Events. In IJCAI. IJCAI/AAAI Press, 1396--1403.

5. Yixiang Chen, Linhao Ye, Yufeng Ye, Peng Zhang, and Qinfeng Tan. 2022. Anomaly Detection from Log Data Sequences with Perturbations. In DSC. IEEE, 183--190.

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