An Effective Parallel SVM Intrusion Detection Model for Imbalanced Training Datasets

Jing Zhao, Jun Li, Chun Long, Jinxia Wei, Guanyao Du, Wei Wan, Yue Wang

Abstract

In the field of network security, the Intrusion Detection Systems (IDSs) always require more research on detection models and algorithms to improve system performance. Meanwhile, higher quality data is critical to the accuracy of detection models. In this paper, an effective parallel SVM intrusion detection model with feature reduction for imbalanced datasets is proposed. The model includes 3 parts: 1) NKSMOTE-a Modified unbalanced data processing method. 2) feature reduction based on Correlation Analysis. 3) Parallel SVM algorithm combining clustering and classification. The NSL-KDD dataset is used to evaluate the proposed method, and the empirical results show that it achieves a better and more robust performance than existing methods in terms of the accuracy, detection rate, false alarm rate and training speed.

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Paper Citation


in Harvard Style

Zhao J., Li J., Long C., Wei J., Du G., Wan W. and Wang Y. (2020). An Effective Parallel SVM Intrusion Detection Model for Imbalanced Training Datasets.In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-423-7, pages 225-232. DOI: 10.5220/0009390302250232


in Bibtex Style

@conference{iceis20,
author={Jing Zhao and Jun Li and Chun Long and Jinxia Wei and Guanyao Du and Wei Wan and Yue Wang},
title={An Effective Parallel SVM Intrusion Detection Model for Imbalanced Training Datasets},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2020},
pages={225-232},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009390302250232},
isbn={978-989-758-423-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - An Effective Parallel SVM Intrusion Detection Model for Imbalanced Training Datasets
SN - 978-989-758-423-7
AU - Zhao J.
AU - Li J.
AU - Long C.
AU - Wei J.
AU - Du G.
AU - Wan W.
AU - Wang Y.
PY - 2020
SP - 225
EP - 232
DO - 10.5220/0009390302250232