Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree Model

TLDR

Machine‑learning intrusion detection systems are often black‑box, limiting trust; explainable AI is needed to reveal data evidence and causal reasoning, yet prior work focuses mainly on accuracy rather than insight. This study applies explainable decision‑tree models to improve trust management in IDS. The authors train interpretable decision trees on the KDD benchmark, extract human‑readable rules, and compare their accuracy to state‑of‑the‑art algorithms.

Abstract

Despite the growing popularity of machine learning models in the cyber‐security applications (e.g., an intrusion detection system (IDS)), most of these models are perceived as a black‐box. The eXplainable Artificial Intelligence (XAI) has become increasingly important to interpret the machine learning models to enhance trust management by allowing human experts to understand the underlying data evidence and causal reasoning. According to IDS, the critical role of trust management is to understand the impact of the malicious data to detect any intrusion in the system. The previous studies focused more on the accuracy of the various classification algorithms for trust in IDS. They do not often provide insights into their behavior and reasoning provided by the sophisticated algorithm. Therefore, in this paper, we have addressed XAI concept to enhance trust management by exploring the decision tree model in the area of IDS. We use simple decision tree algorithms that can be easily read and even resemble a human approach to decision‐making by splitting the choice into many small subchoices for IDS. We experimented with this approach by extracting rules in a widely used KDD benchmark dataset. We also compared the accuracy of the decision tree approach with the other state‐of‐the‐art algorithms.