Unsupervised Learning for Network Flow Based Anomaly Detection in the Era of Deep Learning-Reference-Cited by-同舟云学术

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Unsupervised Learning for Network Flow Based Anomaly Detection in the Era of Deep Learning

Published:2020-08 Issue: Volume: Page:
ISSN:
Container-title:2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService)
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Author:

Kabir Md. Ahsanul,Luo Xiao

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9173519/9179488/09179547.pdf?arnumber=9179547

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Variational Autoencoders (VAEs) for Anomaly Detection;Advances in Digital Crime, Forensics, and Cyber Terrorism;2024-09-13

2. A Holistic review and performance evaluation of unsupervised learning methods for network anomaly detection;International Journal on Smart Sensing and Intelligent Systems;2024-04-01

3. Exploring the Boundaries of Lateral Movement Detection Through Unsupervised Learning;2024

4. MIM-GAN-based Anomaly Detection for Multivariate Time Series Data;2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall);2023-10-10

5. Assessment of Deep Packet Inspection System of Network traffic and Anomaly Detection;International Journal of Scientific Research in Science, Engineering and Technology;2023-06-06

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