ICASSP 2021accepted0 citations
Voting-Based Ensemble Model for Network Anomaly Detection
Tzu-Hsin Yang, Yu-Tai Lin, Chao-Lun Wu, Chih-Yu Wang
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.
BibTeX
@inproceedings{icassp2021_votingbasedensem,
title = {Voting-Based Ensemble Model for Network Anomaly Detection},
author = {Tzu-Hsin Yang and Yu-Tai Lin and Chao-Lun Wu and Chih-Yu Wang},
booktitle = {ICASSP 2021},
year = {2021}
}