Few-Shot Malware Traffic Classification Method Using Network Traffic and Meta Transfer Learning-Reference-Cited by-同舟云学术

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Few-Shot Malware Traffic Classification Method Using Network Traffic and Meta Transfer Learning

Published:2022-09 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)
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Author:

Guo Hanyi¹,Zhang Xixi¹,Wang Yu¹,Adebisi Bamidele²,Gacanin Haris³,Gui Guan¹

Affiliation:

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

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

3. RWTH Aachen University,Institute for Communication Technologies and Embedded Systems,Aachen,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10012685/10012692/10012914.pdf?arnumber=10012914

Reference26 articles.

1. A Novel Malware Traffic Classification Method using Semi-Supervised Learning

2. Semisupervised Federated-Learning-Based Intrusion Detection Method for Internet of Things

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

4. Federated Deep Learning for Zero-Day Botnet Attack Detection in IoT Edge Devices

5. A Survey of Payload-Based Traffic Classification Approaches

Cited by 5 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. Classifying attack traffic in IoT environments via few-shot learning;Journal of Information Security and Applications;2024-06

2. Specific Emitter Identification Using Adaptive Signal Feature Embedded Knowledge Graph;IEEE Internet of Things Journal;2024-02-01

3. IoT Botnet-Traffic Classification Using Few-Shot Learning;2023 IEEE International Conference on Big Data (BigData);2023-12-15

4. A Few-Shot Malware Traffic Classification Method Using Knowledge Transfer Learning;2023 IEEE 23rd International Conference on Communication Technology (ICCT);2023-10-20

5. SPN: A Method of Few-Shot Traffic Classification With Out-of-Distribution Detection Based on Siamese Prototypical Network;IEEE Access;2023

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