EAGER: SaTC-EDU: Exploring Visualized and Explainable Artificial Intelligence to Improve Students’ Learning Experience in Digital Forensics Education

EAGER：SaTC-EDU：探索可视化和可解释的人工智能，以改善学生在数字取证教育中的学习体验

• 批准号: 2039289
• 负责人: Weifeng Xu
• 金额: $ 14.5万
• 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: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2039289&HistoricalAwards=false
• 关键词: EAGER; SaTC; EDU; Exploring; Visualized

With the exponential increase in cybercrimes in recent years, the need for Computer Forensics and Digital Evidence (CFDE) expertise is rapidly growing. A qualified CFDE professional needs to have deep knowledge of digital forensic evidence identification, acquisition, and examination, as well as the ability to present and explain digital forensic evidence in courtrooms. However, there are major barriers to instilling the core knowledge of CFDE and practice of cyber investigation techniques in a diverse body of interested students. For example, a systematic approach for collecting, organizing, and analyzing digital forensic evidence is lacking. This project will engage novel interdisciplinary perspectives, including artificial intelligence (AI), cybersecurity, criminal justice, and computer science to re-examine the emerging CFDE field with a formal approach. This project will then explore visualized and explainable AI to improve students’ learning experience in digital forensics education at Minority-Serving Institutions (MSIs) including Historically Black Colleges and Universities (HBCUs).The project brings together faculty from the University of Baltimore, an MSI, Bowie State University, one of the oldest HBCUs in Maryland, and the University of Missouri Kansas City, who have synergistic expertise in digital forensics, cybersecurity, AI, law, and computer science. The project will leverage graph-based AI models to provide students with visualized depictions of forensic evidence, the patterns of evidence, and the connections among the evidence. It will also explore explainable AI to support the development of forensic evidence that is accountable and presentable to courts, and develop AI-aided CFDE instructional materials. The project will address research questions at the intersection of AI, CFDE, and education including the following: (a) How do graph-based models store, retrieve, and present digital forensic evidence? (b) How do graph-based AI models discover new evidence and to what extent should we trust AI-discovered evidence/patterns? (c) How can knowledge and techniques of AI-assisted investigation be infused into CFDE instructional materials, and to what extent do the materials improve students’ learning experiences? Learning materials will be made available to both the CFDE and data science communities. This project is supported by a special initiative of the Secure and Trustworthy Cyberspace (SaTC) program to foster new, previously unexplored, collaborations between the fields of cybersecurity, artificial intelligence, and education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy.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.

近年来，随着网络犯罪呈指数级增长，对计算机取证和数字证据 (CFDE) 专业知识的需求迅速增长。合格的 CFDE 专业人员需要对数字取证证据识别、获取和检查以及数字取证证据拥有深入的了解。然而，向感兴趣的不同群体灌输 CFDE 的核心知识和网络调查技术的实践存在重大障碍，例如收集、组织和分析的系统方法。数字分析法医证据该项目将采用新颖的跨学科视角，包括人工智能（AI）、网络安全、刑事司法和计算机科学，以正式的方法重新审视新兴的 CFDE 领域，然后探索可视化和可解释的人工智能以改进。学生在包括历史黑人学院和大学 (HBCU) 在内的少数族裔服务机构 (MSI) 中的数字取证教育学习体验。该项目汇集了来自 MSI 巴尔的摩大学、历史最悠久的黑人大学之一鲍伊州立大学的教师马里兰州的 HBCU 和密苏里大学堪萨斯城分校在数字取证、网络安全、人工智能、法律和计算机科学方面拥有协同专业知识，该项目将利用基于图形的人工智能模型为学生提供取证证据的可视化表示。它还将探索可解释的人工智能，以支持对法庭负责和可呈现的法医证据的开发，并开发人工智能辅助的 CFDE 教学材料。该项目将解决交叉点的研究问题。人工智能， CFDE 和教育包括以下内容：（a）基于图的模型如何存储、检索和呈现数字取证证据（b）基于图的人工智能模型如何发现新证据以及我们应该在多大程度上信任人工智能发现的证据？ (c) 如何将人工智能辅助调查的知识和技术融入 CFDE 教学材料中，这些材料将在多大程度上改善学生的学习体验？该项目得到了社区的支持。安全可信网络空间 (SaTC) 计划的一项特别举措，旨在促进网络安全、人工智能和教育领域之间新的、以前未探索过的合作。SaTC 计划与联邦网络安全研究与发展战略计划和国家隐私保持一致。研究战略旨在保护和维护网络系统不断增长的社会和经济效益，同时确保安全和隐私。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。