Many or Few Samples?: Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification
Author:
Affiliation:
1. Universitá degli Studi di Napoli Federico II, Huawei Technology France
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10198870/10198880/10198965.pdf?arnumber=10198965
Reference48 articles.
1. A few shots traffic classification with mini-FlowPic augmentations
2. How to achieve high classification accuracy with just a few labels: A semi-supervised approach using sampled packets;rezaei;ICDM,2019
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4. Bootstrap your own latent - a new approach to self-supervised learning;grill;NeurIPS,0
5. A closer look at few-shot classification;chen;ICLRE,2019
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1. CoMDet: A Contrastive Multimodal Pre-Training Approach to Encrypted Malicious Traffic Detection;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02
2. Self-Supervised Traffic Classification: Flow Embedding and Few-Shot Solutions;IEEE Transactions on Network and Service Management;2024-06
3. MetaRockETC: Adaptive Encrypted Traffic Classification in Complex Network Environments via Time Series Analysis and Meta-Learning;IEEE Transactions on Network and Service Management;2024-04
4. Data Augmentation for Traffic Classification;Lecture Notes in Computer Science;2024
5. IoT Botnet-Traffic Classification Using Few-Shot Learning;2023 IEEE International Conference on Big Data (BigData);2023-12-15
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