Fast Neural Intrusion Detection System Based on Hidden Weight Optimization Algorithm and Feature Selection
Mansour Sheikhan · 2009
Abstract: Intrusion detection is known as an essential component to secure the systems in information and communication technology (ICT). In this paper, two mechanisms are used to achieve a fast intrusion detection system (IDS): 1) the training speed of neural attack classifier is improved by using output weight optimization-hidden weight optimization (OWO-HWO) training algorithm, 2) a feature relevance analysis is performed to decrease the number of input features and size of neural classifier. Experimental results show that the proposed system improves classification rates, especially for remote-to-local (R2L) attack category and is effective in terms of detection rate (DR) and cost per example (CPE). False alarm rate (FAR) of the proposed system is comparable with other intrusion detection systems, as well. Key words: Intrusion detection • neural model • fast training • feature selection