[2603.00222] Empowering Future Cybersecurity Leaders: Advancing Students through FINDS Education for Digital Forensic Excellence

[2603.00222] Empowering Future Cybersecurity Leaders: Advancing Students through FINDS Education for Digital Forensic Excellence

arXiv - AI 4 min read

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Abstract page for arXiv paper 2603.00222: Empowering Future Cybersecurity Leaders: Advancing Students through FINDS Education for Digital Forensic Excellence

Computer Science > Cryptography and Security arXiv:2603.00222 (cs) [Submitted on 27 Feb 2026] Title:Empowering Future Cybersecurity Leaders: Advancing Students through FINDS Education for Digital Forensic Excellence Authors:Yashas Hariprasad, Subhash Gurappa, Sundararaj S. Iyengar, Jerry F. Miller, Pronab Mohanty, Naveen Kumar Chaudhary View a PDF of the paper titled Empowering Future Cybersecurity Leaders: Advancing Students through FINDS Education for Digital Forensic Excellence, by Yashas Hariprasad and 5 other authors View PDF HTML (experimental) 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...

Originally published on March 03, 2026. Curated by AI News.

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