Lu, H. orcid.org/0009-0009-9245-1389, Dong, Y. orcid.org/0000-0003-4898-331X, Wu, Z. et al. (2 more authors) (2025) New class detection in network traffic classification using confidence information embedded cascade structure. IEEE Transactions on Network Science and Engineering. ISSN 2327-4697
Abstract
Network traffic classification plays an important role in network management. With continued emergence of new applications, classifiers need to deal with unknown classes in an open set environment. However, the available open set flow recognition methods cannot well balance the performance of new class detection and the fine-grained classification of known classes. Moreover, these methods could pursue high accuracy at the cost of the classification speed. To address these problems, this paper proposes an unknown network traffic detection method based on confidence (difference) and a cascade structure, by analyzing the confidence distributions of both the known and new classes. The proposed method works as follows. Firstly, it uses a cascade structure to detect new class samples (having high confidence) which are difficult to identify using existing methods; secondly, it employs the maximum confidence difference to classify the new and known classes. In order to better detect new classes with high confidence, an algorithm is designed to select the pseudo-negative samples from the unlabelled dataset with an adaptive threshold. The proposed method is evaluated on real-world datasets. The results show that compared with the state-of-the-art methods, the proposed method can significantly improve the overall accuracy and the classification latency is also greatly reduced.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Authors. Except as otherwise noted, this author-accepted version of a journal article published in IEEE Transactions on Network Science and Engineering is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | open set flow recognition; confidence difference; new class detection; unlabelled dataset; network traffic classification |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Feb 2025 16:39 |
Last Modified: | 17 Feb 2025 16:39 |
Status: | Published online |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/tnse.2025.3538564 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:223412 |
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Filename: TNSE-2024-04-0506 Final Accepted Manuscript.pdf
Licence: CC-BY 4.0