Autoencoder ensembles for network intrusion detection
Author:
Affiliation:
1. CAS (Chinese Academy of Sciences),Computer Network Information Center,China
Funder
Chinese Academy of Sciences
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9728250/9728768/09728934.pdf?arnumber=9728934
Reference24 articles.
1. Autoencoder-based feature learning for cyber security applications
2. A Deep Learning Approach for Network Intrusion Detection System
3. Building Auto-Encoder Intrusion Detection System based on random forest feature selection
4. Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
5. Support-vector networks
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1. A Comprehensive Review of Dimensionality Reduction Techniques for Real time Network Intrusion Detection with Applications in Cybersecurity;Defence Science Journal;2024-03-26
2. NFHP-RN: A Method of Few-Shot Network Attack Detection Based on the Network Flow Holographic Picture-ResNet;Computer Modeling in Engineering & Sciences;2024
3. An Effective Method for Detecting Unknown Types of Attacks Based on Log-Cosh Variational Autoencoder;Applied Sciences;2023-11-19
4. Autoencoder Feature Residuals for Network Intrusion Detection: One-Class Pretraining for Improved Performance;Machine Learning and Knowledge Extraction;2023-07-31
5. An intrusion detection method based on stacked sparse autoencoder and improved gaussian mixture model;Computers & Security;2023-05
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