ABSTRACT
Research into network anomaly detection methods in few-shot scenarios is of great practical significance, since there are relatively rare network anomaly events compared with normal ones. Sample augmentation based on GAN (Generative Adversarial network) has few requirements for domain knowledge and data types, and also has strong universality. In order to address the lack of guidance in hyperparameters optimizing for sample augmentation based on GAN, the joint optimization of GAN and classifier is introduced to realize the network anomaly sample augmentation and anomaly detection. Firstly, the network anomaly sample augmentation model and network anomaly detection classifier based on GAN are designed. Subsequently, hyperparameters trained with the sample augmentation model are optimized and selected according to the performance of the classifier on the augmented sample set. Finally, experiments are conducted on two types of datasets, namely network performance parameter characteristics and network traffic data characteristics. In accordance with the experimental results, the proposed sample augmentation method based on the joint optimization could effectively improve the accuracy of network anomaly detection in the few-shot scenarios.
- Xiao, S., Zhan, Y., Zhang, B. (2019) Event-triggered networked fault detection for positive Markovian systems. Signal Processing, 157: 161--169.Google ScholarDigital Library
- Thaha, M., Riaz, A.S. (2017) An analysis of fault detection strategies in wireless sensor networks. Journal of Network and Computer Applications, 78: 267--287.Google ScholarDigital Library
- Garshasbi, M.S. (2017) Fault localization based on combines active and passive measurements in computer networks by ant colony optimization. Reliability Engineering and System Safety, 152: 205--212.Google ScholarCross Ref
- Debashish, M., Bidyadhar, S., Raju, D. (2020) Real-time sensor fault detection in Tokamak using different machine learning algorithms. Fusion Engineering and Design, 151: 1--8.Google Scholar
- Rumelhart, D., Hinton, G., Williams, R. (1986) Learning representations by back-propagating errors. Nature, 323: 533--536.Google ScholarCross Ref
- Bengio, Y., Lamblin, P., Dan, P. (2007) Greedy layer-wise training of deep networks. Advances in Neural Information Processing Systems, 19: 153--160.Google ScholarDigital Library
- Dai, J., Wang, J., Zhu, Z. (2019) Anomaly detection of mechanical systems based on generative adversarial network and auto-encoder. Chinese Journal of Scientific Instrument, 40: 16--26.Google Scholar
- Yan, K., Chong, A., Mo, Y. (2020) Generative Adversarial Network for Fault Detection Diagnosis of Chillers. Building and Environment, 8: 1--18.Google Scholar
- Zhang, W., Li, X., Jia, X. (2020) Machinery fault diagnosis with imbalanced data using deep generative adversarial networks. Measurement, 152: 377--388.Google ScholarCross Ref
- Zhu, X., Zhang, P. (2020) Fault detection and diagnosis method for heterogeneous wireless network based on GAN. Journal on Communications, 41: 110--119.Google Scholar
Index Terms
- Network Anomaly Detection Method Based on Joint Optimization of GAN and Classifier in Few-shot Scenarios
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