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. 54S pp. 301982
This paper proposes a standardized methodology for evaluating the performance of Large Language Models (LLMs) in digital forensic timeline analysis tasks, such as event summarization. The methodology includes a dataset, timeline generation, and ground truth development, and recommends the use of BLEU and ROUGE metrics for quantitative evaluation.
Forensic Science International: Digital Investigation Vol. 52 pp. 301859
This study explores the potential of Large Language Models (LLMs) in improving digital forensic investigation efficiency, addressing challenges such as bias, explainability, censorship, and resource-intensive infrastructure. A comprehensive literature review highlights the current challenges in digital forensics and the possibilities of incorporating LLMs, with a focus on established models, methods, and key challenges.
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.