2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDCHLG-2.3
Paper Title AN ACCURACY NETWORK ANOMALY DETECTION METHOD BASED ON ENSEMBLE MODEL
Authors Fengrui Liu, Xuefei Li, Wei Xiong, Haiyang Jiang, Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences, China; Gaogang Xie, Computer Network Information Center, Chinese Academy of Sciences; University of Chinese Academy of Sciences, China
SessionCHLG-2: ZYELL - NCTUNetwork Anomaly Detection Challenge
LocationZoom
Session Time:Monday, 07 June, 13:00 - 14:45
Presentation Time:Monday, 07 June, 13:00 - 14:45
Presentation Poster
Topic Grand Challenge: ZYELL - NCTUNetwork Anomaly Detection Challenge
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Identifying network anomaly detection is important since they may carry critical information in circumstances such as a burst of intrusions, privacy theft, system damage and fraudulent activities. In recent years, there are many detection methods for network anomalies are proposed, however, a single model always faces the problems of over or under-fitting, high bias and variance. An improved method is to comprehensively use the results of multiple models and then reform the final predictions. This paper introduces an ensemble model, which is a powerful technique to increase accuracy on network anomaly detection. By combining three base models Xgboost, LightGBM and Catboost into one anomaly detector, we successfully detect different DDOS-smurf and Probing activities. This ensemble model is verified on ZYELL-NCTU net traffic, which is a large-scale dataset for read-world network anomaly detection. All code are open source in Github and can be directly run on Colab Jupyter Notebook.