Network Anomaly Detection Using Federated Learning and Transfer Learning
- Publisher:
- Springer
- Publication Type:
- Conference Proceeding
- Citation:
- Communications in Computer and Information Science, 2020, 1268 CCIS, pp. 219-231
- Issue Date:
- 2020-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Binder1.pdf | Published version | 918.05 kB |
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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 learning-based 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, 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, 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.
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