A Novel Model for Anomaly Detection in Network Traffic Based on Support Vector Machine and Clustering

New vulnerabilities and ever-evolving network attacks pose great threats to today’s cyberspace security. Anomaly detection in network traffic is a promising and effective technique to enhance network security. In addition to traditional statistical analysis and rule-based detection techniques, machi...

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Autores principales:	Qian Ma, Cong Sun, Baojiang Cui
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology (General) T1-995 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/e38e84a0d3ae47fd95319e56777aeb79
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spelling	oai:doaj.org-article:e38e84a0d3ae47fd95319e56777aeb792021-11-29T00:56:24ZA Novel Model for Anomaly Detection in Network Traffic Based on Support Vector Machine and Clustering1939-012210.1155/2021/2170788https://doaj.org/article/e38e84a0d3ae47fd95319e56777aeb792021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2170788https://doaj.org/toc/1939-0122New vulnerabilities and ever-evolving network attacks pose great threats to today’s cyberspace security. Anomaly detection in network traffic is a promising and effective technique to enhance network security. In addition to traditional statistical analysis and rule-based detection techniques, machine learning models are introduced for intelligent detection of abnormal traffic data. In this paper, a novel model named SVM-C is proposed for the anomaly detection in network traffic. The URLs in the network traffic log are transformed into feature vectors via statistical laws and linear projection. The obtained feature vectors are fed into a support vector machine (SVM) classifier and classified as normal or abnormal. Based on the idea of SVM and clustering, we construct an optimization model to train the parameters of the feature extraction method and traffic classifier. Numerical tests indicate that the proposed model outperforms the state of the arts on all the tested datasets.Qian MaCong SunBaojiang CuiHindawi-WileyarticleTechnology (General)T1-995Science (General)Q1-390ENSecurity and Communication Networks, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995 Science (General) Q1-390
spellingShingle	Technology (General) T1-995 Science (General) Q1-390 Qian Ma Cong Sun Baojiang Cui A Novel Model for Anomaly Detection in Network Traffic Based on Support Vector Machine and Clustering
description	New vulnerabilities and ever-evolving network attacks pose great threats to today’s cyberspace security. Anomaly detection in network traffic is a promising and effective technique to enhance network security. In addition to traditional statistical analysis and rule-based detection techniques, machine learning models are introduced for intelligent detection of abnormal traffic data. In this paper, a novel model named SVM-C is proposed for the anomaly detection in network traffic. The URLs in the network traffic log are transformed into feature vectors via statistical laws and linear projection. The obtained feature vectors are fed into a support vector machine (SVM) classifier and classified as normal or abnormal. Based on the idea of SVM and clustering, we construct an optimization model to train the parameters of the feature extraction method and traffic classifier. Numerical tests indicate that the proposed model outperforms the state of the arts on all the tested datasets.
format	article
author	Qian Ma Cong Sun Baojiang Cui
author_facet	Qian Ma Cong Sun Baojiang Cui
author_sort	Qian Ma
title	A Novel Model for Anomaly Detection in Network Traffic Based on Support Vector Machine and Clustering
title_short	A Novel Model for Anomaly Detection in Network Traffic Based on Support Vector Machine and Clustering
title_full	A Novel Model for Anomaly Detection in Network Traffic Based on Support Vector Machine and Clustering
title_fullStr	A Novel Model for Anomaly Detection in Network Traffic Based on Support Vector Machine and Clustering
title_full_unstemmed	A Novel Model for Anomaly Detection in Network Traffic Based on Support Vector Machine and Clustering
title_sort	novel model for anomaly detection in network traffic based on support vector machine and clustering
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/e38e84a0d3ae47fd95319e56777aeb79
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A Novel Model for Anomaly Detection in Network Traffic Based on Support Vector Machine and Clustering

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