The Hierarchical Ensemble Model for Network Intrusion Detection in the Real-world Dataset
Lei Chen, Shao-En Weng, Chu-Jun Peng, Yin-Chi Li, Hong-Han Shuai, Wen-Huang Cheng · 2022
Network intrusion detection is an indispensable defense in the critical era fulling of cyberattacks. However, it faces a severe class imbalanced issue, and most of the researches are conducted on simulated data. Therefore, this work introduces a hierarchical ensemble architecture with machine learning approaches. It is trained on the latest and real-world dataset to solve the above problems. The experiments show that we outperform state-of-the-art methods on real network traffic data.