The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pencerobohan ialah salah satu isu keselamatan utama internet dengan pertumbuhan pesat dalam peranti pintar dan Internet of Thing (IoT), dan menjadi penting untuk mengesan serangan dan menetapkan penggera dalam sistem IoT. Dalam makalah ini, kaedah berasaskan mesin vektor sokongan (SVM) dan analisis komponen utama (PCA) digunakan untuk mengesan serangan dalam sistem IoT pintar. SVM dengan skema tak linear digunakan untuk klasifikasi pencerobohan dan PCA diguna pakai untuk pemilihan ciri pada set data latihan dan ujian. Eksperimen pada dataset NSL-KDD menunjukkan bahawa ketepatan ujian kaedah yang dicadangkan boleh mencapai 82.2% dengan 16 ciri dipilih daripada PCA untuk pengelasan binari yang hampir sama dengan keputusan yang diperolehi dengan kesemua 41 ciri; dan ketepatan ujian boleh mencapai 78.3% dengan 29 ciri dipilih daripada PCA untuk pelbagai klasifikasi manakala 79.6% tanpa pemilihan ciri. Ketepatan pengesanan serangan Denial of Service (DoS) bagi kaedah yang dicadangkan boleh mencapai peningkatan 8.8% berbanding kaedah berasaskan rangkaian saraf tiruan sedia ada.
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
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
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
Salinan
@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},}
Salinan
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 -