Digital forensics in law enforcement: A case study of LLM-driven evidence analysis

IF 2 4区 医学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Kyung-Jong Kim , Chan-Hwi Lee , So-Eun Bae , Ju-Hyun Choi , Wook Kang
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引用次数: 0

Abstract

The advent of digital technology and the ubiquity of mobile devices in today's society has led to a significant increase in the importance of mobile forensics in criminal investigations. Responding to the escalating volume and complexity of data due to enhanced smartphone capabilities and pervasive messaging apps, law enforcement agencies face challenges in data analysis. This study explores improving investigative efficiency through LLM-driven analysis of text from mobile messenger communications. We have conducted experiments on anonymized data collected from real crime scenes by employing three state-of-the-art LLM models, namely GPT-4o, Gemini 1.5 and Claude 3.5. The study focuses on optimizing model performance by employing prompt engineering, interpreting expressions embedded with hidden meanings such as slang, and contextually inferring ambiguous word usage. Finally, model performance is quantitatively evaluated using metrics such as precision, recall, F1 score, and hallucination rate.
执法中的数字取证:法学硕士驱动的证据分析案例研究
数字技术的出现和移动设备在当今社会的无处不在,使得移动取证在刑事调查中的重要性显著增加。由于智能手机功能的增强和无处不在的消息应用程序,数据量和复杂性不断增加,执法机构在数据分析方面面临挑战。本研究探讨了通过法学硕士驱动的移动信使通信文本分析来提高调查效率。我们利用三种最先进的法学硕士模型,即gpt - 40, Gemini 1.5和Claude 3.5,对从真实犯罪现场收集的匿名数据进行了实验。该研究的重点是通过使用提示工程,解释包含隐藏含义的表达(如俚语)以及上下文推断歧义词的使用来优化模型性能。最后,使用精度、召回率、F1分数和幻觉率等指标对模型性能进行定量评估。
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来源期刊
CiteScore
5.90
自引率
15.00%
发文量
87
审稿时长
76 days
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