A Deep Auto-Encoder based LightGBM Approach for Network Intrusion Detection System

Kun Mo, Jian Li

2019

Abstract

With the development of the network in recent years, cyber security has become one of the most challenging aspects of modern society. Machine learning is one of extensively used techniques in Intrusion Detection System, which has achieved comparable performance. To extract more important features, this paper proposes an efficient model based Auto-Encoder and LightGBM to classify network traffic. KDD99 dataset [1], as the benchmark dataset, is used for computing the performance and analyse the metrics of the method. Based on Auto-Encoder, we extract more important features, and then mix them with existing features to improve the effectiveness of the LightGBM [2] model. The experimental results show that the proposed algorithm produces the best performance in terms of overall accuracy.

Paper Citation

in Harvard Style

Mo K. and Li J. (2019). A Deep Auto-Encoder based LightGBM Approach for Network Intrusion Detection System.In Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - Volume 1: CTISC, ISBN 978-989-758-357-5, pages 142-147. DOI: 10.5220/0008098401420147

in Bibtex Style

@conference{ctisc19,
author={Kun Mo and Jian Li},
title={A Deep Auto-Encoder based LightGBM Approach for Network Intrusion Detection System},
booktitle={Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - Volume 1: CTISC,},
year={2019},
pages={142-147},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008098401420147},
isbn={978-989-758-357-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - Volume 1: CTISC,
TI - A Deep Auto-Encoder based LightGBM Approach for Network Intrusion Detection System
SN - 978-989-758-357-5
AU - Mo K.
AU - Li J.
PY - 2019
SP - 142
EP - 147
DO - 10.5220/0008098401420147