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
2024 Cyber Research Conference - Ireland (Cyber-RCI)
This study investigates the effectiveness of colour-based descriptors in Content-Based Image Retrieval (CBIR) for human trafficking image analysis. The research evaluates the impact of various parameters on image matching accuracy, achieving a Top-50 accuracy of over 95% on the Hotels-50K dataset. The approach demonstrates potential in advancing image analysis tools for human trafficking investigations and other contexts.
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.
Digital Forensics Doctoral Symposium
AutoDFBench is an automated framework for testing and evaluating AI-generated digital forensic code and tools. It validates AI-generated code against NIST''s Computer Forensics Tool Testing Program (CFTT) procedures and calculates a benchmarking score. The framework operates in four phases: data preparation, API handling, code execution, and result recording with score calculation.