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A hybrid unsupervised clustering-based anomaly detection method

Journal Article


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.

Publication Date


  • 2021

Citation


  • Pu, G., Wang, L., Shen, J., & Dong, F. (2021). A hybrid unsupervised clustering-based anomaly detection method. Tsinghua Science and Technology, 26(2), 146-153. doi:10.26599/TST.2019.9010051

Scopus Eid


  • 2-s2.0-85089350269

Start Page


  • 146

End Page


  • 153

Volume


  • 26

Issue


  • 2

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.

Publication Date


  • 2021

Citation


  • Pu, G., Wang, L., Shen, J., & Dong, F. (2021). A hybrid unsupervised clustering-based anomaly detection method. Tsinghua Science and Technology, 26(2), 146-153. doi:10.26599/TST.2019.9010051

Scopus Eid


  • 2-s2.0-85089350269

Start Page


  • 146

End Page


  • 153

Volume


  • 26

Issue


  • 2