Internet Traffic Classification with Federated Learning
Open accessAbstract
As Internet traffic classification is a typical problem for ISPs or mobile carriers, there have been a lot of studies based on statistical packet header information, deep packet inspection, or machine learning. Due to recent advances in end-to-end encryption and dynamic port policies, machine or deep learning has been an essential key to improve the accuracy of packet classification. In addition, ISPs or mobile carriers should carefully deal with the privacy issue while collecting user packets for accounting or security. The recent development of distributed machine learning, called federated learning, collaboratively carries out machine learning jobs on the clients without uploading data to a central server. Although federated learning provides an on-device learning framework towards user privacy protection, its feasibility and performance of Internet traffic classification have not been fully examined. In this paper, we propose a federated-learning traffic classification protocol (FLIC), which can achieve an accuracy comparable to centralized deep learning for Internet application identification without privacy leakage. FLIC can classify new applications on-the-fly when a participant joins in learning with a new application, which has not been done in previous works. By implementing the prototype of FLIC clients and a server with TensorFlow, the clients gather packets, perform the on-device training job and exchange the training results with the FLIC server. In addition, we demonstrate that federated learning-based packet classification achieves an accuracy of 88% under non-independent and identically distributed (non-IID) traffic across clients. When a new application that can be classified dynamically as a client participates in learning was added, an accuracy of 92% was achieved.
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FLIC can classify new applications on the fly with an accuracy of 88% under non-IID traffic across clients.
“Tensorflow FLIC can classify new applications on the fly with an accuracy of 88% under non-IID traffic across clients.”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [41]
In experiments, federated learning achieved 89% accuracy, while centralized DNN achieved 93% accuracy.
“In the experiments, FL achieved 89% accuracy, while in centralized training 93% accuracy is for DNN.”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [41]
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- Authors
- Mun, H., & Lee, Y.
- Affiliations
- Chungnam National University (KR)
- Corresponding author
- Lee, Youngseok
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2020-12-28
- Volume
- 10
- Issue
- 1
- Pages
- 27
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W3117687414
- Cited by
- 52
- References
- 19
- Citations by year
- 2026: 3 · 2025: 13 · 2024: 12 · 2023: 11 · 2022: 10 · 2021: 3
- FWCI
- 2.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Science and ICT, South Korea
- Institute for Information and Communications Technology Promotion
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- internet traffic classification, federated learning, application identification, on-device learning, user privacy protection, non-IID data, packet classification, distributed learning
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