EFS-DNN: An Ensemble Feature Selection-Based Deep Learning Approach to Network Intrusion Detection System

In recent years, the scale of networks has substantially evolved due to the rapid development of infrastructures in real networks. Under the circumstances, intrusion detection systems (IDSs) have become the crucial tool to detect cyberattacks, malicious actions, and anomaly behaviors that threaten the credibility and integrity of information services in networks. The feature selection technologies are commonly applied in various intrusion detection algorithms owing to the potential of improving performance and speeding up decision-making. However, existing feature selection-based intrusion detection methods still suffer from high computational complexity or the lack of robustness. To mitigate these challenges, we propose a novel ensemble feature selection-based deep neural network (EFS-DNN) to detect attacks in networks with high-volume traffic data. In particular, we leverage light gradient boosting machine (LightGBM) as the base selector in the ensemble feature selection module to enhance the robustness of the selected optimal subset. Besides, we utilize a deep neural network with batch normalization and embedding technique as the classifier to improve the expressiveness. We conduct extensive experiments on three public datasets to demonstrate the superiority of the EFS-DNN compared with baselines.