An Effective Intrusion Detection Model based on Random Forest Algorithm with I-SMOTE

Weijinxia, Longchun, Longchun, Wanwei, Wanwei, Zhaojing, Duguanyao, Duguanyao, Yangfan

Abstract

With the wide applications of network in our daily lives, network security is becoming increasing prominent. Intrusion detection systems have been widely used to detect various types of malicious network which cannot be detected by a conventional firewall. Therefore, various machine-learning techniques have been proposed to improve the performance of intrusion detection system. However, the balance of different data classes is critical and will affect detection performance. In order to reduce the impact of class imbalance of the intrusion dataset, this paper proposes a scheme that applies the improved synthetic minority oversampling technique (I-SMOTE) to balance the dataset, employs correlation analysis and random forest to reduce features and uses the random forest algorithm to train the classifier for detection. The experimental results based on the NSL-KDD dataset show that it achieves a better and more robust performance in terms of accuracy, detection rate, false alarms and training speed.

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Paper Citation


in Harvard Style

Weijinxia., Longchun., Wanwei., Zhaojing., Duguanyao. and Yangfan. (2021). An Effective Intrusion Detection Model based on Random Forest Algorithm with I-SMOTE. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-509-8, pages 175-182. DOI: 10.5220/0010393801750182


in Bibtex Style

@conference{iceis21,
author={Weijinxia and Longchun and Wanwei and Zhaojing and Duguanyao and Yangfan},
title={An Effective Intrusion Detection Model based on Random Forest Algorithm with I-SMOTE},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2021},
pages={175-182},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010393801750182},
isbn={978-989-758-509-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - An Effective Intrusion Detection Model based on Random Forest Algorithm with I-SMOTE
SN - 978-989-758-509-8
AU - Weijinxia.
AU - Longchun.
AU - Wanwei.
AU - Zhaojing.
AU - Duguanyao.
AU - Yangfan.
PY - 2021
SP - 175
EP - 182
DO - 10.5220/0010393801750182