@inproceedings{93c1b910469941d7bbfe25e68c9eef96,
title = "A novel intrusion detection system based on extreme machine learning and multi-voting technology",
abstract = "With the fast development of networking technology, billions of devices have been developed with network function. When the scale of a network traffic grows by an order of magnitude, traditional intrusion detection system (IDS) are no longer effective to detect malicious network intrusions. In our work, we propose a novel network intrusion detection framework based on extreme learning machine (ELM) and multi-voting technology (MVT). Due to the real time feature of ELM, several independent ELM networks can be trained simultaneously. The final results are obtained by MVT strategy. The standard UNSW-NB15 data set has been used to evaluate the performance of the proposed method. The experimental result illustrated that the high accuracy can be achieved by using the proposed method.",
keywords = "Extreme Learning Machine (ELM), Intrusion Detection System (IDS), Multi-Voting Technology (MVT), UNSW-NB15",
author = "Jianlei Gao and Senchun Chai and Chen Zhang and Baihai Zhang and Lingguo Cui",
note = "Publisher Copyright: {\textcopyright} 2019 Technical Committee on Control Theory, Chinese Association of Automation.; 38th Chinese Control Conference, CCC 2019 ; Conference date: 27-07-2019 Through 30-07-2019",
year = "2019",
month = jul,
doi = "10.23919/ChiCC.2019.8865258",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "8909--8914",
editor = "Minyue Fu and Jian Sun",
booktitle = "Proceedings of the 38th Chinese Control Conference, CCC 2019",
address = "United States",
}