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

Author

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  • Basim Mahbooba
  • Mohan Timilsina
  • Radhya Sahal
  • Martin Serrano
  • Ahmed Mostafa Khalil

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.

Suggested Citation

  • Basim Mahbooba & Mohan Timilsina & Radhya Sahal & Martin Serrano & Ahmed Mostafa Khalil, 2021. "Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree Model," Complexity, Hindawi, vol. 2021, pages 1-11, January.
  • Handle: RePEc:hin:complx:6634811
    DOI: 10.1155/2021/6634811
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    Cited by:

    1. Jérôme Darmont & Boris Novikov & Robert Wrembel & Ladjel Bellatreche, 2022. "Advances on Data Management and Information Systems," Information Systems Frontiers, Springer, vol. 24(1), pages 1-10, February.
    2. Frank Cremer & Barry Sheehan & Michael Fortmann & Arash N. Kia & Martin Mullins & Finbarr Murphy & Stefan Materne, 2022. "Cyber risk and cybersecurity: a systematic review of data availability," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(3), pages 698-736, July.
    3. Thi-Minh-Trang Huynh & Chuen-Fa Ni & Yu-Sheng Su & Vo-Chau-Ngan Nguyen & I-Hsien Lee & Chi-Ping Lin & Hoang-Hiep Nguyen, 2022. "Predicting Heavy Metal Concentrations in Shallow Aquifer Systems Based on Low-Cost Physiochemical Parameters Using Machine Learning Techniques," IJERPH, MDPI, vol. 19(19), pages 1-21, September.
    4. Nida Aslam & Irfan Ullah Khan & Samiha Mirza & Alanoud AlOwayed & Fatima M. Anis & Reef M. Aljuaid & Reham Baageel, 2022. "Interpretable Machine Learning Models for Malicious Domains Detection Using Explainable Artificial Intelligence (XAI)," Sustainability, MDPI, vol. 14(12), pages 1-22, June.

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