Project info
- Developer Louise Allan
- Showcase year 2025
- Programme Computing
This project explores the integration of artificial intelligence (AI) into digital forensic investigations to enhance the efficiency of timeline visualisation. By using unsupervised learning algorithms, the system automates the identification of suspicious events within large datasets, reducing human error and speeding up analysis. The AI-driven process is combined with interactive timeline visualisations, providing a clear and detailed representation of events related to a crime. This innovation aims to address the challenges of managing growing data volumes, standardising forensic data, and improving overall investigation accuracy, ultimately supporting digital forensic investigators in their critical work.
What motivated me to develop this project is the real-world challenge investigators face when dealing with the overwhelming amount of digital evidence in modern cases. With so much data to analyse, it is easy for important details to be overlooked, leading to inefficiencies and errors. The lack of standardisation across forensic tools makes it even harder to piece everything together. I wanted to take my existing interest in AI and explore how it could help ease this burden, automate the process of identifying suspicious events, and ultimately improve how forensic timelines are visualised. This project is all about making investigations faster, more accurate and less stressful for the people undertaking such important work.
After graduation, I hope to pursue a PhD, continue development of my research, and eventually return to education as a teacher with my own experiences and passions to pass along.