A Hybrid Unsupervised Clustering-Based Anomaly Detection Method

查看全文 作　　者：Guo [1]Pu;Lijuan [1]Wang;Jun [2]Shen;Fang [3]Dong

机构地区：[1]School of Cyber Engineering,Xidian University,Xi'an 710126,China;[2]School of Computing and Information Technology,University of Wollongong,NSW 2522,Australia;[3]School of Computer Science and Engineering,Southeast University,Nanjing 211189,China

出　　处：《Tsinghua Science and Technology》索引2021年第26卷第2期,共8页

基　　金：supported in part by the National Natural Science Foundation of China(Nos.61702398 and 61872079);China 111 Project(No.B16037);University Global Partnership Network(UGPN)Project of the University of Wollongong 2018–2019。 摘　　要：In recent years,machine learning-based cyber intrusion detection methods have gained increasing popularity.The number and complexity of new attacks continue to rise;therefore,effective and intelligent solutions are necessary.Unsupervised machine learning techniques are particularly appealing to intrusion detection systems since they can detect known and unknown types of attacks as well as zero-day attacks.In the current paper,we present an unsupervised anomaly detection method,which combines Sub-Space Clustering(SSC)and One Class Support Vector Machine(OCSVM)to detect attacks without any prior knowledge.The proposed approach is evaluated using the well-known NSL-KDD dataset.The experimental results demonstrate that our method performs better than some of the existing techniques. 关键词：unsupervised learning CLUSTERING intrusion detection feature selection

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A Hybrid Unsupervised Clustering-Based Anomaly Detection Method

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