Robust Wireless Network Anomaly Detection with Collaborative Adversarial Autoencoders
Author:
Affiliation:
1. The University of Melbourne,Department of Electrical and Electronic Engineering,Australia
2. School of Computing and Information Systems, The University of Melbourne,Australia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10278505/10278554/10278930.pdf?arnumber=10278930
Reference32 articles.
1. A Semi-Supervised Learning Approach for Network Anomaly Detection in Fog Computing
2. An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks;goodfellow;ArXiv,2015
3. Wireless Network Simulation to Create Machine Learning Benchmark Data
4. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
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