The Forensics Investigations Network in Digital Sciences (FINDS) Research Center of Excellence (CoE), funded by the U.S. Army Research Laboratory, advances Digital Forensic Engineering Education (DFEE) through an integrated research education framework for AI enabled cybersecurity workforce development. FINDS combines high performance computing (HPC), secure software engineering, adversarial analytics, and experiential learning to address emerging cyber and synthetic media threats. This paper introduces the Multidependency Capacity Building Skills Graph (MCBSG), a directed acyclic graph based model that encodes hierarchical and cross domain dependencies among competencies in AI-driven forensic programming, statistical inference, digital evidence processing, and threat detection. The MCBSG enables structured modeling of skill acquisition pathways and quantitative capacity assessment. Supervised machine learning methods, including entropy-based Decision Tree Classifiers and regression modeling, are applied to longitudinal multi cohort datasets capturing mentoring interactions, laboratory performance metrics, curriculum artifacts, and workshop participation. Feature importance analysis and cross validation identify key predictors of technical proficiency and research readiness. Three year statistical evaluation demonstrates significant gains in forensic programming accuracy, adversarial reasoning, and HPC-enabled investigative workflows. Results validate the MCBSG as a scalable, interpretable framework for data-driven, inclusive cybersecurity education aligned with national defense workforce priorities.

翻译：由美国陆军研究实验室资助的数字科学取证调查网络卓越研究中心，通过集成研究教育框架推进数字取证工程教育，旨在培养具备人工智能能力的网络安全人才队伍。FINDS融合高性能计算、安全软件工程、对抗性分析与体验式学习，以应对新兴网络威胁与合成媒体威胁。本文提出多依赖能力建设技能图——一种基于有向无环图的模型，用于编码人工智能驱动取证编程、统计推断、数字证据处理与威胁检测等领域能力间的层次化及跨域依赖关系。MCBSG支持技能获取路径的结构化建模与量化能力评估。研究采用监督机器学习方法（包括基于熵的决策树分类器与回归模型），对纵向多队列数据集进行分析，该数据集涵盖导师互动、实验室绩效指标、课程成果及研讨会参与情况。通过特征重要性分析与交叉验证，识别出技术熟练度与研究准备度的关键预测因子。为期三年的统计评估显示，学生在取证编程准确性、对抗性推理及高性能计算驱动的调查工作流程方面取得显著提升。研究结果验证了MCBSG作为一种可扩展、可解释的框架，能够实现数据驱动且包容性的网络安全教育，并与国防人才队伍建设重点保持一致。