@inproceedings{7e460849947a4ebab4577a8cad4bf9dd,
title = "The Hierarchical Ensemble Model for Network Intrusion Detection in the Real-world Dataset",
abstract = "Network intrusion detection is an indispensable defense in the critical era fulling of cyberattacks. However, it faces a severe class imbalanced issue, and most of the researches are conducted on simulated data. Therefore, this work introduces a hierarchical ensemble architecture with machine learning approaches. It is trained on the latest and real-world dataset to solve the above problems. The experiments show that we outperform state-of-the-art methods on real network traffic data.",
author = "Lei Chen and Weng, {Shao En} and Peng, {Chu Jun} and Li, {Yin Chi} and Shuai, {Hong Han} and Cheng, {Wen Huang}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022 ; Conference date: 27-05-2022 Through 01-06-2022",
year = "2022",
doi = "10.1109/ISCAS48785.2022.9937322",
language = "English",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2983--2987",
booktitle = "IEEE International Symposium on Circuits and Systems, ISCAS 2022",
address = "美國",
}