Deep Learning Based Latent Feature Extraction for Intrusion Detection

Abstract

Despite the attraction of considerable interest from researchers and industries, the community still faces the problem of building reliable and efficient IDSs, capable of detecting intrusion with high accuracy and low time consuming. In this paper, we are investigating a hybrid scheme that combines advantages of deep learning methods and support vector machine to improve the accuracy and efficiency. Initially, a method of deep learning, such as stacked Auto-encoder (SAE) network, is utilized to reduce the dimensionality of the feature sets and gain the latent features. This is followed by a support vector machine (SVM) for binary classification of the events into normal or attacks. Our method is implemented and evaluated using ISCX IDS UNB dataset. Experimental result indicated that our combined method outperforms SVM alone in terms of both accuracy and run-time efficiency.