Anomaly Detection in 5G using Variational Autoencoders-Reference-Cited by-同舟云学术

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Anomaly Detection in 5G using Variational Autoencoders

Published:2024-06-17 Issue: Volume: Page:1-6
ISSN:
Container-title:2024 Silicon Valley Cybersecurity Conference (SVCC)
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Author:

Islam Amanul¹,Chang Sang-Yoon²,Kim Jinoh³,Kim Jonghyun⁴

Affiliation:

1. University of Malaya,Department of Computer Science and Information Technology,Kuala Lumpur,Malaysia,50603

2. University of Colorado,Department of Computer Science,Colorado Springs,CO,US,80918

3. Texas A&M University-Commerce (TAMUC),Computer Science Department,Commerce,TX,USA,75428

4. ETRI,Cybersecurity Division,Daejeon,Korea,34129

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10637325/10637304/10637312.pdf?arnumber=10637312

Reference14 articles.

1. Deep Learning Anomaly Detection for Cellular IoT With Applications in Smart Logistics

2. A Survey on Machine Learning-Based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks

3. Machine Learning With Variational AutoEncoder for Imbalanced Datasets in Intrusion Detection

4. Toward Effective Intrusion Detection Using Log-Cosh Conditional Variational Autoencoder

5. 5G-NIDD: A Comprehensive Network Intrusion Detection Dataset Generated over 5G Wireless Network;Samarakoon,2022

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