Campus Network Intrusion Detection based on Federated Learning
Paper
Paper/Presentation Title | Campus Network Intrusion Detection based on Federated Learning |
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Presentation Type | Paper |
Authors | Chen, Junjun, Guo, Qiang, Fu, Zhongnan, Shang, Qun, Ma, Hao and Wu, Di |
Journal or Proceedings Title | Proceedings of 2022 International Joint Conference on Neural Networks (IJCNN) |
Number of Pages | 8 |
Year | 2022 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
Digital Object Identifier (DOI) | https://doi.org/10.1109/IJCNN55064.2022.9892843 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/abstract/document/9892843 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/9891857/proceeding |
Conference/Event | 2022 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. |
Keywords | intrusion detection; federated learnig; cyber security; imbalanced data; transfer learning |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460407. System and network security |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Peking University, China |
Deakin University |
https://research.usq.edu.au/item/z4y16/campus-network-intrusion-detection-based-on-federated-learning
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