Academic cybersecurity and digital forensics research group spanning University College Dublin and South East Technological University.
The Forensics and Security Research Group conducts research in digital forensics, cybersecurity, network investigation, artificial intelligence for forensic workflows, cloud and IoT forensics, and digital forensic education.
Founded in University College Dublin and now expanded through collaboration with South East Technological University, the group works with academic, law-enforcement, and industry partners on research that improves the reliability, scalability, and practical impact of digital investigations.
Latest
VAAS: A novel dual-module framework for image manipulation detection in digital forensics, combining global attention-based anomaly estimation with patch-level self-consistency scoring for interpretable anomaly detection and tamper localization.
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.
The group has new work appearing at DFRWS EU 2026 and in Forensic Science International: Digital Investigation.
AutoDFBench 1.0 is a comprehensive benchmarking framework for digital forensic tool testing and validation, addressing the lack of standardisation in tool validation and evaluation methodologies. It integrates five areas defined by the NIST CFTT programme and enables fair and reproducible comparison across tools and forensic scripts.
Recent Output
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.
Forensic Science International: Digital Investigation Vol. 57 pp. 302135
This paper investigates digital forensic evidence and regulation of large language models and proprietary AI systems, highlighting the need for AI forensic readiness and examinability.
IEEE Access Vol. 13
This paper proposes a model architecture that integrates image N-dominant colours and colour histogram vectors with image embedding from deep metric learning and classification perspectives to improve image geolocation in indoor scenes.