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

Yashas Hariprasad, Subhash Gurappa, Sundararaj S. Iyengar, Jerry F. Miller, Pronab Mohanty, Naveen Kumar Chaudhary

TL;DR

Results validate the Multidependency Capacity Building Skills Graph as a scalable, interpretable framework for data-driven, inclusive cybersecurity education aligned with national defense workforce priorities.

Abstract

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.

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

TL;DR

Results validate the Multidependency Capacity Building Skills Graph as a scalable, interpretable framework for data-driven, inclusive cybersecurity education aligned with national defense workforce priorities.

Abstract

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.
Paper Structure (37 sections, 4 equations, 13 figures, 2 tables, 2 algorithms)

This paper contains 37 sections, 4 equations, 13 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Motivation of the Work
  3. Motivation Paradigm - Iyengar's Law: Tripling of Cybercrimes Incidents Every 3 Years
  4. Exploring Facts and Observations: A Closer Look at Understanding and Analyzing Cyber Information
  5. Related Works
  6. Research Gaps
  7. Essential Imperative: Cultivating a Robust Workforce for Science Education and Technology Through Development Initiatives
  8. Proposed Framework
  9. Multi-dependency Capacity Building Skills Graph
  10. Case Study - Technology Company Seeking to Bolster its Cybersecurity Capabilities
  11. Methodology
  12. Generalized Framework
  13. Advancing Forensics: AI-Driven Development and Adaptation
  14. Nurturing Excellence: Empowering the Future with an AI-Enabled Workforce Development Initiative
  15. Integration of AI enabled Research and Development Framework
  16. ...and 22 more sections

Figures (13)

  • Figure 1: Exponential Growth Rate of Cyber Crime in a Major City, Adopted from: https://indianexpress.com/article/cities/bangalore/cybercrime-cases-recorded-2022-bengaluru-secures-top-spot-9055838/
  • Figure 2: Comprehensive Approach for Enhancing the Success of Digital Forensics Workforce Development Efforts
  • Figure 3: Example of Multidependency Capacity Building Skills Graph
  • Figure 4: Global Collaborative Hub for Digital Forensics Excellence: The FINDS Network
  • Figure 5: Algorithmic Chart: Key Processes Involved in Digital Forensics Investigation (Sankita et al., 2023)
  • ...and 8 more figures