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
L'intrusion est l'un des problèmes de sécurité majeurs d'Internet avec la croissance rapide des appareils intelligents et de l'Internet des objets (IoT), et il devient important de détecter les attaques et de déclencher des alarmes dans les systèmes IoT. Dans cet article, la méthode basée sur la machine à vecteurs de support (SVM) et l'analyse des composants principaux (PCA) est utilisée pour détecter les attaques dans les systèmes IoT intelligents. SVM avec schéma non linéaire est utilisé pour la classification des intrusions et PCA est adopté pour la sélection des fonctionnalités sur les ensembles de données de formation et de test. Les expériences sur l'ensemble de données NSL-KDD montrent que la précision des tests de la méthode proposée peut atteindre 82.2 % avec 16 caractéristiques sélectionnées dans PCA pour la classification binaire, ce qui est presque le même que le résultat obtenu avec les 41 caractéristiques ; et la précision du test peut atteindre 78.3 % avec 29 fonctionnalités sélectionnées dans PCA pour la multi-classification, tandis qu'elle peut atteindre 79.6 % sans sélection de fonctionnalités. La précision de détection des attaques par déni de service (DoS) de la méthode proposée peut atteindre une amélioration de 8.8 % par rapport à la méthode existante basée sur un réseau neuronal artificiel.
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
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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
Copier
@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},}
Copier
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 -