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Authors: Jinxia Wei ; Chun Long ; Wei Wan ; Yurou Zhang ; Jing Zhao and Guanyao Du

Affiliation: Department of Security, Computer Network Information Center, Chinese Academy of Sciences, Beijing and China

ISBN: 978-989-758-372-8

Keyword(s): Intrusion Detection, Random Forest (RF), Correlation Analysis, Logarithm Marginal Density Ratio.

Abstract: Intrusion detection systems are essential in the field of network security. To improve the performance of detection model, many machine learning algorithms have been applied to intrusion detection models. Higher-quality data is critical to the accuracy of detection model and could greatly improve the performance. In this paper, an effective random forest-based intrusion detection algorithm with feature reduction and transformation is proposed. Specifically, we implement the correlation analysis and logarithm marginal density ratio to reduce and strengthen the original features respectively, which can greatly improve accuracy rate of classifier. The proposed classification system was deployed on NSL-KDD dataset. The experimental results show that this paper achieves better results than other related methods in terms of false alarm rate, accuracy, detection rate and running time.

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Paper citation in several formats:
Wei, J.; Long, C.; Wan, W.; Zhang, Y.; Zhao, J. and Du, G. (2019). An Effective RF-based Intrusion Detection Algorithm with Feature Reduction and Transformation.In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-372-8, pages 221-228. DOI: 10.5220/0007718602210228

@conference{iceis19,
author={Jinxia Wei. and Chun Long. and Wei Wan. and Yurou Zhang. and Jing Zhao. and Guanyao Du.},
title={An Effective RF-based Intrusion Detection Algorithm with Feature Reduction and Transformation},
booktitle={Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2019},
pages={221-228},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007718602210228},
isbn={978-989-758-372-8},
}

TY - CONF

JO - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - An Effective RF-based Intrusion Detection Algorithm with Feature Reduction and Transformation
SN - 978-989-758-372-8
AU - Wei, J.
AU - Long, C.
AU - Wan, W.
AU - Zhang, Y.
AU - Zhao, J.
AU - Du, G.
PY - 2019
SP - 221
EP - 228
DO - 10.5220/0007718602210228

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