loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Jing Zhao 1 ; 2 ; Jun Li 1 ; 2 ; Chun Long 1 ; 2 ; Jinxia Wei 1 ; Guanyao Du 1 ; 2 ; Wei Wan 1 ; 2 and Yue Wang 1 ; 2

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

Keyword(s): Intrusion Detection, Feature Reduction, Parallel SVM, Classification.

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.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 44.192.107.255

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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; ISSN 2184-4992, SciTePress, pages 225-232. DOI: 10.5220/0009390302250232

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

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
IS - 2184-4992
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
PB - SciTePress