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 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 \(O_t\), 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, \(O_t\) 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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Zhao, Y., Chen, J., Guo, Q., Teng, J., Wu, D. (2020). Network Anomaly Detection Using Federated Learning and Transfer Learning. In: Yu, S., Mueller, P., Qian, J. (eds) Security and Privacy in Digital Economy. SPDE 2020. Communications in Computer and Information Science, vol 1268. Springer, Singapore. https://doi.org/10.1007/978-981-15-9129-7_16
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DOI: https://doi.org/10.1007/978-981-15-9129-7_16
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