A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhancing transparency and interpretability in cybersecurity
The rise of sophisticated cyber threats has spurred advancements in Intrusion Detection Systems (IDS), which are crucial for identifying and mitigating security breaches in real-time. Traditional IDS often rely on complex machine learning algorithms that lack transparency despite their high accuracy...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1526221/full |
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| _version_ | 1850085556195688448 |
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| author | Vincent Zibi Mohale Ibidun Christiana Obagbuwa |
| author_facet | Vincent Zibi Mohale Ibidun Christiana Obagbuwa |
| author_sort | Vincent Zibi Mohale |
| collection | DOAJ |
| description | The rise of sophisticated cyber threats has spurred advancements in Intrusion Detection Systems (IDS), which are crucial for identifying and mitigating security breaches in real-time. Traditional IDS often rely on complex machine learning algorithms that lack transparency despite their high accuracy, creating a “black box” effect that can hinder the analysts’ understanding of their decision-making processes. Explainable Artificial Intelligence (XAI) offers a promising solution by providing interpretability and transparency, enabling security professionals to understand better, trust, and optimize IDS models. This paper presents a systematic review of the integration of XAI in IDS, focusing on enhancing transparency and interpretability in cybersecurity. Through a comprehensive analysis of recent studies, this review identifies commonly used XAI techniques, evaluates their effectiveness within IDS frameworks, and examines their benefits and limitations. Findings indicate that rule-based and tree-based XAI models are preferred for their interpretability, though trade-offs with detection accuracy remain challenging. Furthermore, the review highlights critical gaps in standardization and scalability, emphasizing the need for hybrid models and real-time explainability. The paper concludes with recommendations for future research directions, suggesting improvements in XAI techniques tailored for IDS, standardized evaluation metrics, and ethical frameworks prioritizing security and transparency. This review aims to inform researchers and practitioners about current trends and future opportunities in leveraging XAI to enhance IDS effectiveness, fostering a more transparent and resilient cybersecurity landscape. |
| format | Article |
| id | doaj-art-082a3ba858d3412aaada7c62933ed7f0 |
| institution | DOAJ |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-082a3ba858d3412aaada7c62933ed7f02025-08-20T02:43:42ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-01-01810.3389/frai.2025.15262211526221A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhancing transparency and interpretability in cybersecurityVincent Zibi MohaleIbidun Christiana ObagbuwaThe rise of sophisticated cyber threats has spurred advancements in Intrusion Detection Systems (IDS), which are crucial for identifying and mitigating security breaches in real-time. Traditional IDS often rely on complex machine learning algorithms that lack transparency despite their high accuracy, creating a “black box” effect that can hinder the analysts’ understanding of their decision-making processes. Explainable Artificial Intelligence (XAI) offers a promising solution by providing interpretability and transparency, enabling security professionals to understand better, trust, and optimize IDS models. This paper presents a systematic review of the integration of XAI in IDS, focusing on enhancing transparency and interpretability in cybersecurity. Through a comprehensive analysis of recent studies, this review identifies commonly used XAI techniques, evaluates their effectiveness within IDS frameworks, and examines their benefits and limitations. Findings indicate that rule-based and tree-based XAI models are preferred for their interpretability, though trade-offs with detection accuracy remain challenging. Furthermore, the review highlights critical gaps in standardization and scalability, emphasizing the need for hybrid models and real-time explainability. The paper concludes with recommendations for future research directions, suggesting improvements in XAI techniques tailored for IDS, standardized evaluation metrics, and ethical frameworks prioritizing security and transparency. This review aims to inform researchers and practitioners about current trends and future opportunities in leveraging XAI to enhance IDS effectiveness, fostering a more transparent and resilient cybersecurity landscape.https://www.frontiersin.org/articles/10.3389/frai.2025.1526221/fullintrusion detection systemscyber threatsexplainable artificial intelligencesystematic reviewmodel explainabilitymodel interpretability |
| spellingShingle | Vincent Zibi Mohale Ibidun Christiana Obagbuwa A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhancing transparency and interpretability in cybersecurity Frontiers in Artificial Intelligence intrusion detection systems cyber threats explainable artificial intelligence systematic review model explainability model interpretability |
| title | A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhancing transparency and interpretability in cybersecurity |
| title_full | A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhancing transparency and interpretability in cybersecurity |
| title_fullStr | A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhancing transparency and interpretability in cybersecurity |
| title_full_unstemmed | A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhancing transparency and interpretability in cybersecurity |
| title_short | A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhancing transparency and interpretability in cybersecurity |
| title_sort | systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhancing transparency and interpretability in cybersecurity |
| topic | intrusion detection systems cyber threats explainable artificial intelligence systematic review model explainability model interpretability |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1526221/full |
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