A Hybrid Unsupervised Clustering-Based Anomaly Detection Method

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.