Generative Adversarial Network-Based Network Anomaly Detection with Unlabeled Data
Author:
Affiliation:
1. China Unicom Intelligent Network Innovation Center,Beijing,China,100048
2. Beijing University of Posts and Telecommunications,School of Information and Communication Engineering,Beijing,China,100876
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10287388/10287413/10287473.pdf?arnumber=10287473
Reference9 articles.
1. Federated-Learning-Enabled Intelligent Fog Radio Access Networks: Fundamental Theory, Key Techniques, and Future Trends
2. Semisynchronous personalized federated learning over mobile edge networks;you;IEEE Transactions on Wireless Communications,2022
3. Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
4. Towards Cost Minimization for Wireless Caching Networks With Recommendation and Uncharted Users’ Feature Information
5. Anomaly detection
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