Feature Selection for Intrusion Detection Using Random Forest

An intrusion detection system collects and analyzes information from different areas within a computer or a network to identify possible security threats that include threats from both outside as well as inside of the organization. It deals with large amount of data, which contains various ir-relevant and redundant features and results in increased processing time and low detection rate. Therefore, feature selection should be treated as an indispensable pre-processing step to improve the overall system performance significantly while mining on huge datasets. In this context, in this paper, we focus on a two-step approach of feature selection based on Random Forest. The first step selects the features with higher variable importance score and guides the initialization of search process for…