EI-XIDS: An explainable intrusion detection system based on integration framework
Yang J. Xu, Chen Li, Kun Zhang, Haojun Xia, Bibo Tu · 2024
The application of Deep Learning (DL) in Intrusion Detection Systems (IDS) has become a focal point of research due to its outstanding performance. However, the black-box nature of these systems has raised concerns within the research community. Addressing this challenge, this paper draws upon the concept of ensemble learning and introduces an Explainable Intrusion Detection System (X-IDS), EI-XIDS. This system integrates a variety of advanced Explainable Artificial Intelligence (XAI) methods and adaptively selects them according to different scenarios through reinforcement learning. Comparative experiments demonstrate that EI-XIDS outperforms the current state-of-the-art explanation methods, achieving label flip rates of 97% and 96% on the NSL-KDD and UNSW-NB15 datasets, respectively. These results underscore EI-XIDS’s superior interpretability accuracy, robustness, and sparsity, showcasing its significant potential in the field of network security.