A Lightweight Intrusion Detection Model Based on Feature Selection and Maximum Entropy Model

Abstract

Intrusion detection is a critical component of secure information systems. Current intrusion detection systems (IDS) especially NIDS (Network Intrusion Detection System) examine all data features to detect intrusions. However, some of the features may be redundant or contribute little to the detection process and therefore they have great impact on the system performance. This paper proposes a lightweight intrusion detection model that is computationally efficient and effective based on feature selection and Maximum Entropy (ME) model. Firstly, the issue of identifying important input features is addressed. Since elimination of the insignificant and/or useless inputs leads to a simplification of the problem, therefore results to faster and more accurate detection. Secondly, classic ME model is used to learn and detect intrusions using the selected important features. Experimental results on the well-known KDD 1999 dataset show the proposed model is effective and can be applied to real-time intrusion detection environments.