Integrated Feature-Based Network Intrusion Detection System Using Incremental Feature Generation

Abstract

Machine learning (ML)-based network intrusion detection systems (NIDSs) depend entirely on the performance of machine learning models. Therefore, many studies have been conducted to improve the performance of ML models. Nevertheless, relatively few studies have focused on the feature set, which significantly affects the performance of ML models. In addition, features are generated by analyzing data collected after the session ends, which requires a significant amount of memory and a long processing time. To solve this problem, this study presents a new session feature set to improve the existing NIDSs. Current session-feature-based NIDSs are largely classified into NIDSs using a single-host feature set and NIDSs using a multi-host feature set. This research merges two different session feature sets into an integrated feature set, which is used to train an ML model for the NIDS. In addition, an incremental feature generation approach is proposed to eliminate the delay between the session end time and the integrated feature creation time. The improved performance of the NIDS using integrated features was confirmed through experiments. Compared to a NIDS based on ML models using existing single-host feature sets and multi-host feature sets, the NIDS with the proposed integrated feature set improves the detection rate by 4.15% and 5.9% on average, respectively.