Local Augmentation with Functionality-Preservation for Semi-Supervised Graph Intrusion Detection
Author:
Affiliation:
1. School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10622104/10622158/10622440.pdf?arnumber=10622440
Reference12 articles.
1. E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
2. Application of a Dynamic Line Graph Neural Network for Intrusion Detection With Semisupervised Learning
3. Anomal-E: A self-supervised network intrusion detection system based on graph neural networks
4. Functionality-Preserving Adversarial Machine Learning for Robust Classification in Cybersecurity and Intrusion Detection Domains: A Survey
5. Denoising diffusion probabilistic models;Ho;Advances in Neural Information Processing Systems,2020
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