A Lightweight Semi-Supervised Learning Method Based on Consistency Regularization for Intrusion Detection
Author:
Affiliation:
1. Shanghai Jiao Tong University,School of Electronic Information and Electrical Engineering,Shanghai,China
2. NJUPT,College of Telecommunications and Information Engineering,Nanjing,China
Funder
Shanghai Jiao Tong University
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9837954/9838246/09838567.pdf?arnumber=9838567
Reference17 articles.
1. Nearest cluster-based intrusion detection through convolutional neural networks
2. Semi-supervised learning methods for network intrusion detection
3. Enhancing collaborative intrusion detection via disagreement-based semi-supervised learning in IoT environments
4. A semi-self-taught network intrusion detection system
5. Generative Adversarial Networks For Launching and Thwarting Adversarial Attacks on Network Intrusion Detection Systems
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1. Self-Supervised Learning Malware Traffic Classification Based on Masked Autoencoder;IEEE Internet of Things Journal;2024-05-15
2. The Missing Link in Network Intrusion Detection: Taking AI/ML Research Efforts to Users;IEEE Access;2024
3. Semi-Supervised Learning Based Trust Evaluation for Underwater Wireless Sensor Networks;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2024
4. Differentially-Private Federated Intrusion Detection via Knowledge Distillation in Third-party IoT Systems of Smart Airports;ICC 2023 - IEEE International Conference on Communications;2023-05-28
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