| Paper ID | CHLG-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 |
| Session | CHLG-2: ZYELL - NCTUNetwork Anomaly Detection Challenge |
| Location | Zoom |
| 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 |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
