# Empowering Future Cybersecurity Leaders: Advancing Students through FINDS Education for Digital Forensic Excellence

arXiv: https://arxiv.org/abs/2603.00222 · HTML: https://mlsanity.com/paper/2603.00222/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 27 Feb 2026.

## Builds on (most cited first, 3 of 3)

- [SoK: Exploring the State of the Art and the Future Potential of Artificial Intelligence in Digital Forensic Investigation](https://mlsanity.com/paper/2012.01987/) (2020)
- [Optimal Fault-Tolerant Data Fusion in Sensor Networks: Fundamental Limits and Efficient Algorithms](https://mlsanity.com/paper/2210.04156/) (2022)
- [The Privacy-Utility Tradeoff in Rank-Preserving Dataset Obfuscation](https://mlsanity.com/paper/2305.07079/) (2023)

## Built on by (most cited first, 1 of 1)

- [MedSR-Vision: Deep Learning Framework for Multi-Domain Medical Image Super-Resolution](https://mlsanity.com/paper/2605.03343/) (2026)

## Abstract (arXiv)

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.