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-2.2
Paper Title VOTING-BASED ENSEMBLE MODEL FOR NETWORK ANOMALY DETECTION
Authors Tzu-Hsin Yang, Yu-Tai Lin, Chao-Lun Wu, Chih-Yu Wang, Academia Sinica, Taiwan
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 Network anomaly detection (NAD) aims to capture potential abnormal behaviors by observing traffic data over a period of time. In this work, we propose a machine learning framework based on XGBoost and deep neural networks to classify normal traffic and anomalous traffic. Data-driven feature engineering and post-processing are further proposed to improve the performance of the models. The experiment results suggest the proposed model can achieve 94% for F1 measure in the macro average of five labels on real-world traffic data.