Few-shot Network Anomaly Detection via Cross-network Meta-learning-Reference-Cited by-同舟云学术

Few-shot Network Anomaly Detection via Cross-network Meta-learning

Published:2021-04-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Web Conference 2021
language:
Short-container-title:

Author:

Ding Kaize¹,Zhou Qinghai²,Tong Hanghang²,Liu Huan¹

Affiliation:

1. Arizona State University, USA

2. University of Illinois Urbana-Champaign, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3442381.3449922

Reference54 articles.

1. Graph based anomaly detection and description: a survey

2. Sambaran Bandyopadhyay N Lokesh and M Narasimha Murty. 2019. Outlier aware network embedding for attributed networks. In AAAI. Sambaran Bandyopadhyay N Lokesh and M Narasimha Murty. 2019. Outlier aware network embedding for attributed networks. In AAAI.

3. Markus M Breunig Hans-Peter Kriegel Raymond T Ng and Jörg Sander. 2000. LOF: identifying density-based local outliers. In SIGMOD. Markus M Breunig Hans-Peter Kriegel Raymond T Ng and Jörg Sander. 2000. LOF: identifying density-based local outliers. In SIGMOD.

4. Shaosheng Cao Wei Lu and Qiongkai Xu. 2016. Deep neural networks for learning graph representations. In AAAI. Shaosheng Cao Wei Lu and Qiongkai Xu. 2016. Deep neural networks for learning graph representations. In AAAI.

5. Kaize Ding Jundong Li Nitin Agarwal and Huan Liu. 2020. Inductive anomaly detection on attributed networks. In IJCAI. Kaize Ding Jundong Li Nitin Agarwal and Huan Liu. 2020. Inductive anomaly detection on attributed networks. In IJCAI.

Cited by 59 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Effective graph-neural-network based models for discovering Structural Hole Spanners in large-scale and diverse networks;Expert Systems with Applications;2024-09

2. Locally-adaptive mapping for network alignment via meta-learning;Information Processing & Management;2024-09

3. SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

4. Semi-supervised Graph Anomaly Detection via Multi-view Contrastive Learning;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

5. Graph Anomaly Detection with Domain-Agnostic Pre-Training and Few-Shot Adaptation;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13