Adversarial Learning Aided Malicious Network Traffic Detection Based on EDE Framework-Reference-Cited by-同舟云学术

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Adversarial Learning Aided Malicious Network Traffic Detection Based on EDE Framework

Published:2023-08-10 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CIC International Conference on Communications in China (ICCC)
language:
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Author:

Zhang Lanping¹,Wang Yu¹,Ohtsuki Tomoaki²,Adebisi Bamidele³,Wu Hsiao-Chun⁴,Gui Guan¹

Affiliation:

1. NJUPT,College of Telecommunications and Information Engineering,Nanjing,China

2. Keio University,Department of Information and Computer Science,Yokohama,Japan

3. Manchester Metropolitan University,Faculty of Science and Engineering,Manchester,United Kingdom

4. Louisiana State University,School of Electrical Engineering and Computer Science,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10233255/10233122/10233524.pdf?arnumber=10233524

Reference25 articles.

1. Malware Traffic Classification using Domain Adaptation and Ladder Network for Secure Industrial Internet of Things

2. A Network Traffic anomaly Detection method based on CNN and XGBoost

3. GANomaly: Semi-supervised Anomaly Detection via Adversarial Training

4. Unsupervised Scanning Behavior Detection Based on Distribution of Network Traffic Features Using Robust Autoencoders

5. A Novel Intrusion Detection Method Based on Lightweight Neural Network for Internet of Things

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