A data-driven approach for Network Intrusion Detection and Monitoring based on Kernel Null Space

Abstract

In this study, we propose a new approach to determine intrusions of network in real-time based on statisticalprocess control technique and kernel null space method. The training samples in a class are mapped to a single point using the Kernel Null Foley-Sammon Transform. The Novelty Score are computed from testing samples in order to determine the threshold for the real-time detection of anomaly. The efficiency of theproposed method is illustrated over the KDD99 data set. The experimental results show that our new method outperforms the OCSVM and the original Kernel Null Space method by 1.53% and 3.86% respectively in terms of accuracy.

Keywords

• network security support
• kernel quantile estimator
• one-class classification
• kernel null space vector machine