Detecting Contextual Network Anomalies with Graph Neural Networks-Reference-Cited by-同舟云学术

Detecting Contextual Network Anomalies with Graph Neural Networks

Published:2023-12-05 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2nd on Graph Neural Networking Workshop 2023
language:
Short-container-title:

Author:

Latif Hamid¹^ORCID,Suárez-Varela José²^ORCID,Cabellos-Aparicio Albert¹^ORCID,Barlet-Ros Pere¹^ORCID

Affiliation:

1. Universitat Politècnica de Catalunya, Barcelona, Spain

2. Telefónica Research, Madrid, Spain

Funder

Ministerio de Ciencia e Innovación / Agencia Estatal de Investigación

Spanish Ministry of Economic Affairs and Digital Transformation / European Union

European Union?s Horizon 2020

Catalan Institu- tion for Research and Advanced Studies

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3630049.3630171

Reference23 articles.

1. A survey of network anomaly detection techniques

2. Peter W Battaglia Jessica B Hamrick etal 2018. Relational inductive biases deep learning and graph networks. arXiv preprint arXiv:1806.01261 (2018). Peter W Battaglia Jessica B Hamrick et al. 2018. Relational inductive biases deep learning and graph networks. arXiv preprint arXiv:1806.01261 (2018).

3. Learning long-term dependencies with gradient descent is difficult

4. Monowar H Bhuyan , Dhruba Kumar Bhattacharyya, and Jugal K Kalita . 2013 . Network anomaly detection: methods, systems and tools. IEEE communications surveys & tutorials 16, 1 (2013), 303--336. Monowar H Bhuyan, Dhruba Kumar Bhattacharyya, and Jugal K Kalita. 2013. Network anomaly detection: methods, systems and tools. IEEE communications surveys & tutorials 16, 1 (2013), 303--336.

5. Graph Neural Network-Based Anomaly Detection in Multivariate Time Series