Network anomaly detection using federated learning and transfer learning

Paper

Zhao, Ying, Chen, Junjun, Guo, Qianling, Teng, Jian and Wu, Di. 2020. "Network anomaly detection using federated learning and transfer learning." 1st International Conference on Security and Privacy in Digital Economy (SPDE 2020). Quzhou, Zhejiang, China 30 Oct - 01 Nov 2020 Singapore. Springer. https://doi.org/10.1007/978-981-15-9129-7_16
Paper/Presentation Title

Network anomaly detection using federated learning and transfer learning

Presentation TypePaper
AuthorsZhao, Ying, Chen, Junjun, Guo, Qianling, Teng, Jian and Wu, Di
Journal or Proceedings TitleProceedings of the 1st International Conference on Security and Privacy in Digital Economy (SPDE 2020)
Journal Citation1268, pp. 219-231
Number of Pages13
Year2020
PublisherSpringer
Place of PublicationSingapore
ISBN9789811591280
9789811591297
Digital Object Identifier (DOI)https://doi.org/10.1007/978-981-15-9129-7_16
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007/978-981-15-9129-7_16
Conference/Event1st International Conference on Security and Privacy in Digital Economy (SPDE 2020)
Event Details
1st International Conference on Security and Privacy in Digital Economy (SPDE 2020)
Delivery
In person
Event Date
30 Oct 2020 to end of 01 Nov 2020
Event Location
Quzhou, Zhejiang, China
Event Web Address (URL)
Abstract

Since deep neural networks can learn data representation from training data automatically, deep learning methods are widely used in the network anomaly detection. However, challenges of deep learningbased anomaly detection methods still exist, the major of which is the training data scarcity problem. In this paper, we propose a novel network anomaly detection method (NAFT) using federated learning and transfer learning to overcome the data scarcity problem. In the first learning stage, a people or organization Ot, who intends to conduct a detection model for a specific attack, can join in the federated learning with a similar training task to learn basic knowledge from other participants’ training data. In the second learning stage, Ot uses the transfer learning method to reconstruct and re-train the model to further improve the detection performance on the specific task. Experiments conducted on the UNSW-NB15 dataset show that the proposed method can achieve a better anomaly detection performance than other baseline methods when training data is scarce.

KeywordsNetwork traffic analysis; Federated learning; Transfer learning
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460609. Networking and communications
4602. Artificial intelligence
Public Notes

Files associated with this item cannot be displayed due to copyright restrictions.

SeriesCommunications in Computer and Information Science
Byline AffiliationsBeijing University of Chemical Technology, China
University of Technology Sydney
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