标题： 大型语言模型时代的数字取证 显示英文标题

标题： Digital Forensics in the Age of Large Language Models

摘要： 数字取证在现代调查过程中扮演着至关重要的角色，它利用专门的方法系统地收集、分析和解释数字证据以供司法程序使用。 然而，传统的数字取证技术主要基于劳动密集型的手动过程，随着数字数据的快速增长和复杂性的增加，这些方法变得越来越不足。 为此，大型语言模型（LLMs）作为强大的工具应运而生，能够自动化和增强各种数字取证任务，极大地改变了该领域。 尽管取得了进展，但一般从业者和取证专家往往缺乏对LLM的能力、原则和局限性的全面了解，这限制了LLM在取证应用中的全部潜力。 为填补这一空白，本文旨在提供一个易于理解且系统的概述，介绍LLM如何革新数字取证方法。 具体来说，本文探讨了数字取证的基本概念以及LLM的发展，并强调了LLM的卓越能力。 为了连接理论与实践，本文讨论了相关的实例和现实场景。 我们还批判性地分析了将LLMs应用于数字取证的当前局限性，包括与幻觉、可解释性、偏见和伦理考虑相关的问题。 此外，本文概述了未来研究的前景，强调了在取证过程中有效使用LLMs以实现透明度、问责制和稳健标准化的必要性。 显示英文摘要

摘要： Digital forensics plays a pivotal role in modern investigative processes, utilizing specialized methods to systematically collect, analyze, and interpret digital evidence for judicial proceedings. However, traditional digital forensic techniques are primarily based on manual labor-intensive processes, which become increasingly insufficient with the rapid growth and complexity of digital data. To this end, Large Language Models (LLMs) have emerged as powerful tools capable of automating and enhancing various digital forensic tasks, significantly transforming the field. Despite the strides made, general practitioners and forensic experts often lack a comprehensive understanding of the capabilities, principles, and limitations of LLM, which limits the full potential of LLM in forensic applications. To fill this gap, this paper aims to provide an accessible and systematic overview of how LLM has revolutionized the digital forensics approach. Specifically, it takes a look at the basic concepts of digital forensics, as well as the evolution of LLM, and emphasizes the superior capabilities of LLM. To connect theory and practice, relevant examples and real-world scenarios are discussed. We also critically analyze the current limitations of applying LLMs to digital forensics, including issues related to illusion, interpretability, bias, and ethical considerations. In addition, this paper outlines the prospects for future research, highlighting the need for effective use of LLMs for transparency, accountability, and robust standardization in the forensic process.