Campus Network Intrusion Detection based on Federated Learning

Paper

Chen, Junjun, Guo, Qiang, Fu, Zhongnan, Shang, Qun, Ma, Hao and Wu, Di. 2022. "Campus Network Intrusion Detection based on Federated Learning." 2022 International Joint Conference on Neural Networks (IJCNN). Padua, Italy 18 - 23 Jul 2022 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN55064.2022.9892843
Paper/Presentation Title

Campus Network Intrusion Detection based on Federated Learning

Presentation TypePaper
AuthorsChen, Junjun, Guo, Qiang, Fu, Zhongnan, Shang, Qun, Ma, Hao and Wu, Di
Journal or Proceedings TitleProceedings of 2022 International Joint Conference on Neural Networks (IJCNN)
Number of Pages8
Year2022
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
Digital Object Identifier (DOI)https://doi.org/10.1109/IJCNN55064.2022.9892843
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/abstract/document/9892843
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/9891857/proceeding
Conference/Event2022 International Joint Conference on Neural Networks (IJCNN)
Event Details
2022 International Joint Conference on Neural Networks (IJCNN)
Parent
International Joint Conference on Neural Networks (IJCNN)
Delivery
In person
Event Date
18 to end of 23 Jul 2022
Event Location
Padua, Italy
Abstract

To solve the problem of data scarcity and data silos in campus network intrusion detection, an intrusion detection method based on federated learning is proposed. This method allows multiple participants to collaboratively train a global detection model without sharing their training data with third parties, protecting data privacy. Federated learning is connected to transfer learning, as federated learning allows participants' knowledge transfer via its training mechanism. The resampling method is used in the federated learning training process to improve the global detection model's performance on rare class data. Besides, a contribution evaluation method is proposed, which evaluates participants' contribution in federated learning from two aspects of data quality and quantity. Experimental results show that the proposed method can achieve intrusion detection performance similar to traditional centralized collaborative learning under the premise of protecting participant data privacy.

Keywordsintrusion detection; federated learnig; cyber security; imbalanced data; transfer learning
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460407. System and network security
Public Notes

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

Byline AffiliationsPeking University, China
Deakin University
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