Anomaly-based intrusion detection system

Pattern recognition (psychology)

Intrusion detection system

Support vector machine

Data mining

Machine learning

Artificial intelligence

Psychology

Popularity

Computer science

Cluster analysis

Unsupervised learning

Anomaly detection

Social psychology

Tsinghua Science & Technology

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.

A hybrid unsupervised clustering-based anomaly detection method