Encrypted Mobile Traffic Classification with a Few-shot Incremental Learning Approach

Chen, Y., Tong, Y., Hwee, G. B., Cao, Q. , Razul, S. G. and Lin, Z. (2023) Encrypted Mobile Traffic Classification with a Few-shot Incremental Learning Approach. In: 18th IEEE Conference on Industrial Electronics and Applications (ICIEA 2023), Ningbo, China, 18-22 Aug 2023, ISBN 9798350312201 (doi: 10.1109/ICIEA58696.2023.10241782)

Abstract

Mobile traffic classification is an essential task for network security and management. Even though some progress has been made, the existing methods have limitations regarding plasticity and the requirement for large amounts of labeled data for training. In real-world wireless networks, new applications are constantly emerging. The lack of plasticity means the model must be retrained entirely whenever a larger dataset with new classes is obtained, which is time-consuming. Furthermore, obtaining large amounts of labeled data is often complicated and expensive. To overcome these limitations, we proposed a novel approach for classifying encrypted mobile traffic using the few-shot incremental learning with a Long Short-Term Memory (LSTM) model. We pre-train an LSTM model with a base dataset, then incrementally add classes and update the model with few-shot datasets. We leverage the exemplar selection and knowledge distillation to keep the stability and plasticity of the model. We validate our method by collecting Downlink Control Information (DCI) of twenty different mobile applications from commercial Long Term Evolution (LTE) networks. Our experimental results demonstrate the effectiveness of the proposed method.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Cao, Dr Qi
• Authors: Chen, Y., Tong, Y., Hwee, G. B., Cao, Q., Razul, S. G., and Lin, Z.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 2158-2297
• ISBN: 9798350312201
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in 2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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