Anomaly intrusion detection using one class SVM
Yanxin Wang, Johnny Wong, Andrew S. Miner · 2005
Kernel methods are widely used in statistical learning for many fields, such as protein classification and image processing. We recently extend kernel methods to intrusion detection domain by introducing a new family of kernels suitable for intrusion detection. These kernels, combined with an unsupervised learning method - one-class support vector machine, are used for anomaly detection. Our experiments show that the new anomaly detection methods are able to achieve better accuracy rates than the conventional anomaly detectors.