Title

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

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Link to publisher version (DOI)

http://dx.doi.org/10.26599/TST.2019.9010051