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：人工智能模型的黑箱特性一直是其应用于关键任务时引发诸多担忧的根源。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动提供清晰且可解释说明的机器学习模型。在网络网络安全领域，XAI有潜力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来彻底改变我们处理网络安全的方式。在本综述中，我们回顾了网络系统中面向网络安全的可解释人工智能的最新进展，并探讨了为解决这一重要问题而提出的各种方法。本文遵循对网络驱动网络安全威胁及问题的系统分类进行梳理。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，并指出了未来研究的潜在方向。