SVM Based Intrusion Detection Method with Nonlinear Scaling and Feature Selection

Fei ZHANG; Peining ZHEN; Dishan JING; Xiaotang TANG; Hai-Bao CHEN; Jie YAN

doi:10.1587/transinf.2021EDP7184

SVM Based Intrusion Detection Method with Nonlinear Scaling and Feature Selection

Fei ZHANG, Peining ZHEN, Dishan JING, Xiaotang TANG, Hai-Bao CHEN, Jie YAN

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Intrusion is one of major security issues of internet with the rapid growth in smart and Internet of Thing (IoT) devices, and it becomes important to detect attacks and set out alarm in IoT systems. In this paper, the support vector machine (SVM) and principal component analysis (PCA) based method is used to detect attacks in smart IoT systems. SVM with nonlinear scheme is used for intrusion classification and PCA is adopted for feature selection on the training and testing datasets. Experiments on the NSL-KDD dataset show that the test accuracy of the proposed method can reach 82.2% with 16 features selected from PCA for binary-classification which is almost the same as the result obtained with all the 41 features; and the test accuracy can achieve 78.3% with 29 features selected from PCA for multi-classification while 79.6% without feature selection. The Denial of Service (DoS) attack detection accuracy of the proposed method can achieve 8.8% improvement compared with existing artificial neural network based method.

Publication: IEICE TRANSACTIONS on Information Vol.E105-D No.5 pp.1024-1038

Publication Date: 2022/05/01

Publicized: 2022/02/14

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2021EDP7184

Type of Manuscript: PAPER

Category: Artificial Intelligence, Data Mining

Authors

Fei ZHANG
  Northwestern Polytechnic University
Peining ZHEN
  Shanghai Jiao Tong University
Dishan JING
  Shanghai Jiao Tong University
Xiaotang TANG
  Shanghai Jiao Tong University
Hai-Bao CHEN
  Shanghai Jiao Tong University
Jie YAN
  Northwestern Polytechnic University

Keyword

IoT, support vector machine, principal component analysis, intrusion detection

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Fei ZHANG, Peining ZHEN, Dishan JING, Xiaotang TANG, Hai-Bao CHEN, Jie YAN, "SVM Based Intrusion Detection Method with Nonlinear Scaling and Feature Selection" in IEICE TRANSACTIONS on Information, vol. E105-D, no. 5, pp. 1024-1038, May 2022, doi: 10.1587/transinf.2021EDP7184.
Abstract: Intrusion is one of major security issues of internet with the rapid growth in smart and Internet of Thing (IoT) devices, and it becomes important to detect attacks and set out alarm in IoT systems. In this paper, the support vector machine (SVM) and principal component analysis (PCA) based method is used to detect attacks in smart IoT systems. SVM with nonlinear scheme is used for intrusion classification and PCA is adopted for feature selection on the training and testing datasets. Experiments on the NSL-KDD dataset show that the test accuracy of the proposed method can reach 82.2% with 16 features selected from PCA for binary-classification which is almost the same as the result obtained with all the 41 features; and the test accuracy can achieve 78.3% with 29 features selected from PCA for multi-classification while 79.6% without feature selection. The Denial of Service (DoS) attack detection accuracy of the proposed method can achieve 8.8% improvement compared with existing artificial neural network based method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7184/_p

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@ARTICLE{e105-d_5_1024,
author={Fei ZHANG, Peining ZHEN, Dishan JING, Xiaotang TANG, Hai-Bao CHEN, Jie YAN, },
journal={IEICE TRANSACTIONS on Information},
title={SVM Based Intrusion Detection Method with Nonlinear Scaling and Feature Selection},
year={2022},
volume={E105-D},
number={5},
pages={1024-1038},
abstract={Intrusion is one of major security issues of internet with the rapid growth in smart and Internet of Thing (IoT) devices, and it becomes important to detect attacks and set out alarm in IoT systems. In this paper, the support vector machine (SVM) and principal component analysis (PCA) based method is used to detect attacks in smart IoT systems. SVM with nonlinear scheme is used for intrusion classification and PCA is adopted for feature selection on the training and testing datasets. Experiments on the NSL-KDD dataset show that the test accuracy of the proposed method can reach 82.2% with 16 features selected from PCA for binary-classification which is almost the same as the result obtained with all the 41 features; and the test accuracy can achieve 78.3% with 29 features selected from PCA for multi-classification while 79.6% without feature selection. The Denial of Service (DoS) attack detection accuracy of the proposed method can achieve 8.8% improvement compared with existing artificial neural network based method.},
keywords={},
doi={10.1587/transinf.2021EDP7184},
ISSN={1745-1361},
month={May},}

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TY - JOUR
TI - SVM Based Intrusion Detection Method with Nonlinear Scaling and Feature Selection
T2 - IEICE TRANSACTIONS on Information
SP - 1024
EP - 1038
AU - Fei ZHANG
AU - Peining ZHEN
AU - Dishan JING
AU - Xiaotang TANG
AU - Hai-Bao CHEN
AU - Jie YAN
PY - 2022
DO - 10.1587/transinf.2021EDP7184
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2022
AB - Intrusion is one of major security issues of internet with the rapid growth in smart and Internet of Thing (IoT) devices, and it becomes important to detect attacks and set out alarm in IoT systems. In this paper, the support vector machine (SVM) and principal component analysis (PCA) based method is used to detect attacks in smart IoT systems. SVM with nonlinear scheme is used for intrusion classification and PCA is adopted for feature selection on the training and testing datasets. Experiments on the NSL-KDD dataset show that the test accuracy of the proposed method can reach 82.2% with 16 features selected from PCA for binary-classification which is almost the same as the result obtained with all the 41 features; and the test accuracy can achieve 78.3% with 29 features selected from PCA for multi-classification while 79.6% without feature selection. The Denial of Service (DoS) attack detection accuracy of the proposed method can achieve 8.8% improvement compared with existing artificial neural network based method.
ER -