Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree Model
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 learni...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6634811 |
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| author | Basim Mahbooba Mohan Timilsina Radhya Sahal Martin Serrano |
| author_facet | Basim Mahbooba Mohan Timilsina Radhya Sahal Martin Serrano |
| author_sort | Basim Mahbooba |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-72e1d057e4e249c6bdc7b8d204d59175 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-72e1d057e4e249c6bdc7b8d204d591752025-02-03T01:04:04ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66348116634811Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree ModelBasim Mahbooba0Mohan Timilsina1Radhya Sahal2Martin Serrano3Data Science Institute, Insight Centre for Data Analytics, National University of Ireland Galway, Galway, IrelandData Science Institute, Insight Centre for Data Analytics, National University of Ireland Galway, Galway, IrelandFaculty of Computer Science and Engineering, Hodeidah University, Al Hudaydah, YemenData Science Institute, Insight Centre for Data Analytics, National University of Ireland Galway, Galway, IrelandDespite 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.http://dx.doi.org/10.1155/2021/6634811 |
| spellingShingle | Basim Mahbooba Mohan Timilsina Radhya Sahal Martin Serrano Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree Model Complexity |
| title | Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree Model |
| title_full | Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree Model |
| title_fullStr | Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree Model |
| title_full_unstemmed | Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree Model |
| title_short | Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree Model |
| title_sort | explainable artificial intelligence xai to enhance trust management in intrusion detection systems using decision tree model |
| url | http://dx.doi.org/10.1155/2021/6634811 |
| work_keys_str_mv | AT basimmahbooba explainableartificialintelligencexaitoenhancetrustmanagementinintrusiondetectionsystemsusingdecisiontreemodel AT mohantimilsina explainableartificialintelligencexaitoenhancetrustmanagementinintrusiondetectionsystemsusingdecisiontreemodel AT radhyasahal explainableartificialintelligencexaitoenhancetrustmanagementinintrusiondetectionsystemsusingdecisiontreemodel AT martinserrano explainableartificialintelligencexaitoenhancetrustmanagementinintrusiondetectionsystemsusingdecisiontreemodel |