A Survey on Explainable Artificial Intelligence for Cybersecurity

The "black-box" nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and in...

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Bibliographic Details
Published in:IEEE eTransactions on network and service management 2023-12, Vol.20 (4), p.1-1
Main Authors: Rjoub, Gaith, Bentahar, Jamal, Wahab, Omar Abdel, Mizouni, Rabeb, Song, Alyssa, Cohen, Robin, Otrok, Hadi, Mourad, Azzam
Format: Article
Language:English
Subjects:
Artificial intelligence
Computer security
Cybersecurity
Digital systems
Ethics
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Interpretability
Machine learning
Mathematical models
Robustness
State-of-the-art reviews
Surveys
Systematics
Trustworthiness
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Summary:The "black-box" nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research.
ISSN:1932-4537
1932-4537
DOI:10.1109/TNSM.2023.3282740