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Authors:  Weijinxia 1 ;  Longchun 2 ; 1 ;  Wanwei 2 ; 1 ;  Zhaojing 1 ;  Duguanyao 2 ; 1 and  Yangfan 1

Affiliations: 1 Department of Security, Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100190, China ; 2 Chinese Academy of Sciences University, Beijing, 101408, China

Keyword(s): Intrusion Detection, Class Imbalance, I-SMOTE, Feature Reduction, Random Forest.

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 tra ining speed. (More)

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Paper citation in several formats:
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; ISSN 2184-4992, SciTePress, pages 175-182. DOI: 10.5220/0010393801750182

@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},
issn={2184-4992},
}

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
IS - 2184-4992
AU - Weijinxia.
AU - Longchun.
AU - Wanwei.
AU - Zhaojing.
AU - Duguanyao.
AU - Yangfan.
PY - 2021
SP - 175
EP - 182
DO - 10.5220/0010393801750182
PB - SciTePress