An Ensemble Rule Extraction Algorithm Based on Interpretable Greedy Trees for Detecting Malicious Traffic*

This paper studies the detection and identification problem of malicious network traffic and proposes a rule extraction algorithm based on interpretable models. The algorithm utilizes interpretable greedy trees as the foundational model and extends its applicability to handle multi-classification problems, thereby enhancing interpretability and detection accuracy. This methodology furnishes a more dependable and secure framework for discerning attack categories within the domain of network security. The experiment results show that the proposed algorithm achieves better balance between interpretability and identification precision.