Enhancing IoT-Botnet Detection using Variational Auto-encoder and Cost-Sensitive Learning: A Deep Learning Approach for Imbalanced Datasets-Reference-Cited by-同舟云学术

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Enhancing IoT-Botnet Detection using Variational Auto-encoder and Cost-Sensitive Learning: A Deep Learning Approach for Imbalanced Datasets

Published:2023-09-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Region 10 Symposium (TENSYMP)
language:
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Author:

Wasswa Hassan¹,Lynar Tim¹,Abbass Hussein¹

Affiliation:

1. School of Engineering and Information Technology, University of New South Wales,Canberra,Australia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10223594/10223477/10223613.pdf?arnumber=10223613

Reference28 articles.

1. NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domains

2. SMOTE for high-dimensional class-imbalanced data

3. Towards a rooted subgraph classifier for IoT botnet detection

4. Machine learning-based early detection of IoT botnets using network-edge traffic

5. Telecom Fraud Identification Based on ADASYN and Random Forest

Cited by 3 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. Impact of Latent Space Dimension on IoT Botnet Detection Performance: VAE-Encoder Versus ViT-Encoder;2024 3rd International Conference for Innovation in Technology (INOCON);2024-03-01

2. Enhancing Cybersecurity in the Internet of Things Environment Using Bald Eagle Search Optimization With Hybrid Deep Learning;IEEE Access;2024

3. IoT Botnet Detection: Application of Vision Transformer to Classification of Network Flow Traffic;2023 Global Conference on Information Technologies and Communications (GCITC);2023-12-01

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