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
Digital Forensics Doctoral Symposium
This paper proposes a low-overhead and non-invasive electromagnetic side-channel monitoring approach for forensic-ready industrial control systems. It uses unintentional electromagnetic radiation emitted by Ethernet network cables to detect denial of service attacks with considerable accuracy, introducing an architecture for ICS infrastructure to be forensic-ready with minimal computational resources.
ACM Digital Threats: Research and Practice pp. 3748264
This paper proposes recommendations for fine-tuning large language models (LLMs) for digital forensics tasks, addressing the gap in existing research. A case study on chat summarization showcases the applicability of the recommendations, evaluating multiple fine-tuned models to assess their performance. The study shares lessons learned from the case study, providing insights into the fine-tuning process, computational power issues, data challenges, and evaluation methods.
Proceedings of the 18th International Workshop on Digital Forensics, part of the 20th International Conference on Availability, Reliability and Security
This paper presents an AI-based network forensic readiness framework for resource-constrained environments. The framework integrates optimised artificial intelligence models to detect attacks in real-time, capturing and preserving critical forensic artefacts. It aligns with ISO/IEC 27043:2015 Digital Forensic Readiness principles, reducing time and human effort.