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 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.

翻译：人工智能（AI）模型的“黑箱”特性引发了其在对安全关键型应用中的诸多担忧。可解释人工智能（XAI）是一个快速发展的研究领域，旨在创建能够为其决策和行为提供清晰且可理解解释的机器学习模型。在网络网络安全领域，XAI通过使我们能够更好地理解网络威胁的行为并设计更有效的防御措施，有潜力彻底改变我们应对网络安全的方式。本综述系统回顾了网络系统中面向网络安全的XAI技术最新进展，并探讨了为解决这一重要问题而提出的各种方法。综述遵循对网络驱动型网络安全威胁及问题的系统性分类。我们讨论了当前XAI方法在网络安全背景下的挑战与局限性，并概述了未来研究的有前景方向。