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A Survey on Explainable Artificial Intelligence for Cybersecurity (2303.12942v2)

Published 7 Mar 2023 in cs.CR, cs.AI, and cs.NI

Abstract: The black-box nature of 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 network cybersecurity, XAI has the potential to revolutionize the way we approach network 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 in network systems and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of network-driven cybersecurity threats and issues. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research.

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Authors (8)
  1. Gaith Rjoub (6 papers)
  2. Jamal Bentahar (23 papers)
  3. Omar Abdel Wahab (8 papers)
  4. Rabeb Mizouni (12 papers)
  5. Alyssa Song (1 paper)
  6. Robin Cohen (16 papers)
  7. Hadi Otrok (23 papers)
  8. Azzam Mourad (20 papers)
Citations (15)
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