With the rise of complex cyber devices Cyber Forensics (CF) is facing many new challenges. For example, there are dozens of systems running on smartphones, each with more than millions of downloadable applications. Sifting through this large amount of data and making sense requires new techniques, such as from the field of Artificial Intelligence (AI). To apply these techniques successfully in CF, we need to justify and explain the results to the stakeholders of CF, such as forensic analysts and members of the court, for them to make an informed decision. If we want to apply AI successfully in CF, there is a need to develop trust in AI systems. Some other factors in accepting the use of AI in CF are to make AI authentic, interpretable, understandable, and interactive. This way, AI systems will be more acceptable to the public and ensure alignment with legal standards. An explainable AI (XAI) system can play this role in CF, and we call such a system XAI-CF. XAI-CF is indispensable and is still in its infancy. In this paper, we explore and make a case for the significance and advantages of XAI-CF. We strongly emphasize the need to build a successful and practical XAI-CF system and discuss some of the main requirements and prerequisites of such a system. We present a formal definition of the terms CF and XAI-CF and a comprehensive literature review of previous works that apply and utilize XAI to build and increase trust in CF. We discuss some challenges facing XAI-CF. We also provide some concrete solutions to these challenges. We identify key insights and future research directions for building XAI applications for CF. This paper is an effort to explore and familiarize the readers with the role of XAI applications in CF, and we believe that our work provides a promising basis for future researchers interested in XAI-CF.

翻译：随着复杂网络设备的兴起，网络取证（CF）正面临诸多新挑战。例如，智能手机上运行着数十个系统，每个系统拥有超过数百万个可下载应用程序。要筛选如此海量数据并理解其含义，需要引入人工智能（AI）等新技术。为成功将这些技术应用于网络取证，必须向利益相关方（如取证分析师和法庭成员）证明并解释分析结果，以支持其做出明智决策。若要在网络取证中有效运用AI，需建立对AI系统的信任。此外，确保AI系统的真实性、可解释性、可理解性与交互性，是推动其在网络取证中应用的关键因素。这将使AI系统更易为公众所接受，并确保符合法律标准。可解释AI（XAI）系统可在此发挥关键作用，我们将其命名为XAI-CF。XAI-CF不可或缺，目前仍处于发展初期。本文探讨并论证了XAI-CF的重要价值与优势，强调构建成功且实用的XAI-CF系统的必要性，并讨论了该系统的主要需求与前提条件。我们对网络取证及XAI-CF术语给出了正式定义，系统综述了利用XAI构建并增强网络取证的既往研究，分析了XAI-CF面临的挑战，并提出了具体解决方案。最后，我们总结了构建网络取证XAI应用的核心洞见与未来研究方向。本文旨在探索并让读者熟悉XAI在网络取证中的角色定位，相信我们的工作能为未来有志于XAI-CF的研究者提供坚实基础。