Collaborative Research: Education DCL: EAGER: Harnessing the Power of Large Language Models in Digital Forensics Education at MSI and HBCU

合作研究：教育 DCL：EAGER：在 MSI 和 HBCU 的数字取证教育中利用大型语言模型的力量

• 批准号: 2333949
• 负责人: Weifeng Xu
• 金额: $ 12万
• 依托单位: University of Baltimore
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333949&HistoricalAwards=false
• 关键词: Collaborative; Research; Education; DCL; EAGER

The escalating threat of cybercrime has underscored the urgent need for skilled professionals proficient in collecting and presenting evidence for legal proceedings and business decision-making. However, the vast volume of digital data stored across devices, networks, and social media platforms makes it challenging to locate and analyze specific pieces of evidence. The task of connecting evidence and identifying patterns presents a daunting challenge for human investigators. The novelty of this project lies in harnessing the extraordinary capabilities of Large Language Models (LLMs) to create tailored educational materials for digital forensics professionals and students. These materials are designed to equip investigators with the knowledge and skills necessary to navigate the intricate landscape of cybercrimes and enhance their effectiveness in combating such offenses. The project's broader significance is to better prepare investigators to leverage LLM-assisted techniques for digital forensics, ensuring they can adapt to the evolving nature of cyber threats effectively. The project will fine-tune an LLM to construct Digital Forensic Investigation Graphs (DFIGs) based on criminal cases from a widely recognized repository. These DFIGs serve as visually informative representations of the investigation process, evidence entities, and their interconnections using STIX, a standardized language for exchanging structured threat intelligence data. To ensure accuracy, the entities and relationships within the graphs will undergo scrutiny through graph neural network (GNN) models, identifying and rectifying potential errors. Supported by comprehensive instructional materials, including lecture notes, case studies, and hands-on lab exercises, students will be guided through the process of acquiring the necessary expertise to construct and analyze DFIGs for diverse digital forensic cases. This will promote digital forensics education at the University of Baltimore, a Minority-Serving Institution, and Florida A&M University, an HBCU, among others. Additionally, a faculty development workshop will disseminate the instructional materials to the broader national community, fostering a stronger and more inclusive network of cybercrime fighters.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

网络犯罪的威胁不断升级，突显了迫切需要熟练的专业人员，熟练地收集和提供证据以进行法律程序和商业决策。但是，跨设备，网络和社交媒体平台存储的大量数字数据使找到和分析特定证据的挑战。连接证据和识别模式的任务给人类研究人员带来了艰巨的挑战。该项目的新颖性在于利用大语言模型（LLMS）的非凡能力来为数字取证专业人士和学生创建量身定制的教育材料。这些材料旨在为调查人员提供所需的知识和技能，以浏览网络犯罪的复杂景观并提高其在抗击此类罪行方面的有效性。该项目更广泛的意义是更好地准备研究人员为数字取证提供LLM辅助技术，以确保他们可以有效地适应网络威胁的不断发展的性质。该项目将根据广受认可的存储库中的刑事案件来微调LLM，以构建数字法医调查图（DFIG）。这些DFIG用作调查过程，证据实体及其互连的视觉信息表示，使用Stix（一种用于交换结构化威胁智能数据的标准化语言）。为了确保准确性，图中的实体和关系将通过图神经网络（GNN）模型进行审查，识别和纠正潜在的误差。在包括讲义，案例研究和动手实验室练习在内的全面教学材料的支持下，学生将通过获取必要的专业知识来构建和分析DFIG，以构建和分析多样化的数字法医案例。这将促进巴尔的摩大学的数字取证教育，少数派服务机构和HBCU等佛罗里达州A＆M大学等。此外，教师发展研讨会将向更广泛的民族社区传播教学材料，促进了更强大，更具包容性的网络犯罪战斗机网络。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来支持的。