Abstract
In this study, we propose a new approach to determine intrusions of network in real-time based on statistical process 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 comp
Highlights
Security policies are very important in computer systems to prevent the outside attacks
Intrusion detection system (IDS) is a powerful tool and it attracts the attention of researchers [3]
Our proposed solution aims at improving the performance of the Kernel Null Space method [2] in terms of accuracy
Summary
Security policies are very important in computer systems to prevent the outside attacks. We focus on developing an anomaly-based IDS solution, in which the designed IDS system is trained based on knowledge of normal traffic only. Such a system does not need to be trained with attack data traces to later detect if incoming traffic is anomaly or normal. We propose using a Control-Chart based method called Kernel Quantile Estimator to determine the detection threshold dynamically driven by each specific training data set instead of using a fixed threshold as described in the existing Kernel Null Space solutions [1, 2, 7].
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