Digital Forensics
Digital evidence acquisition and analysis methods that improve efficiency, reliability, automation, and reproducibility.
University College Dublin
Assoc. Prof. Mark Scanlon
Associate Professor
School of Computer Science, University College Dublin
Academic researcher and educator in digital forensics, cybersecurity, and AI-supported investigation.
Research Profile
Digital forensics, cybersecurity, and applied AI
Mark Scanlon is an Associate Professor in University College Dublin’s (UCD’s) School of Computer Science and Founding Director of the Forensics and Security Research Group. He is a Fulbright Scholar in Cybersecurity and Cybercrime Investigation.
His research interests sit at the intersection of digital forensics, cybersecurity, and applied AI, developing and rigorously evaluating methods and tools that make digital evidence acquisition and analysis more efficient, automated, reliable, and reproducible.
Cybersecurity
Security research connected to forensic readiness, cybercrime investigation, and practical investigative workflows.
AI for Forensics
Applied AI methods for evidence processing, investigation support, tool testing, and forensic workflow automation.
Computer Vision for Investigations
Computer vision approaches for digital forensic tasks including image analysis and investigative triage.
Cloud, IoT, and DFaaS
Research on cloud services, Internet of Things devices, Digital Forensics as a Service, and large-scale evidence handling.
Forensic Education
Teaching and curriculum activity in computer forensics, cybercrime investigation, and specialist digital investigation modules.
Recent Output
Selected Publications
2026
Objects as Universal Geolocation Cues: A Computer Vision Approach
13th Annual Digital Forensics Research Workshop Europe (DFRWS EU 2026)
This paper proposes a computer vision approach to geolocation using universal visual cues, specifically electrical plug sockets, to narrow down the search space for law enforcement in combating crimes such as human trafficking and child exploitation.
Publication page2026
AutoDFBench 1.0: A benchmarking framework for digital forensic tool testing and generated code evaluation
Forensic Science International: Digital Investigation Vol. 56 pp. 302055
AutoDFBench 1.0 is a benchmarking framework for digital forensic tool testing, evaluating conventional and AI-generated tools across five areas: string search, deleted file recovery, file carving, Windows registry recovery, and SQLite data recovery.
Publication page2026
Plug to place: Indoor multimedia geolocation from electrical sockets for digital investigation
Forensic Science International: Digital Investigation Vol. 56 pp. 302056
This paper introduces a pipeline for indoor multimedia geolocation using electrical sockets as consistent markers, aiding law enforcement in human trafficking investigations.
Publication page2026
VAAS: Vision-Attention Anomaly Scoring for image manipulation detection in digital forensics
Forensic Science International: Digital Investigation Vol. 56 pp. 302063
VAAS detects image manipulation using Vision Transformers and segmentation embeddings, providing a continuous anomaly score for digital forensics.
Publication pageAcademic Activity
Teaching and Service
Teaching
UCD Modules
Professional Service
Journals and Conferences
- Forensic Science International: Digital Investigation: Senior Editor (2020-Date)
- International Journal of Digital Forensics, Security and Law: Associate Editor (2017-2019); Special Issue Editor (2017); Editorial Board Member (2014-Date)
- Digital Forensics Research Workshop EU (DFRWS EU): Scheduling Chair (2023); Chair (2021); Web Chair (2017); Web Chair (2017); TPC Vice-Chair (2016); TPC Member (2014-Date); Local Arrangements Chair (2015); Session Chair (2015-2017)
- International Conference on Availability, Reliability and Security (ARES): Chair (2024); TPC Member (2013-Date); Session Chair (2015)
Supervision
Prospective PhD Students
Research projects are negotiable with interested students and should be in one of the listed digital forensics research areas.