A hybrid unsupervised clustering-based anomaly detection method
Publication Name
Tsinghua Science and Technology
Abstract
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.
Open Access Status
This publication may be available as open access
Volume
26
Issue
2
Article Number
9147152
First Page
146
Last Page
153
Funding Number
61702398
Funding Sponsor
National Natural Science Foundation of China