Analysing and Visualising (Cyber)crime data using Structured Occurrence Nets and Natural Language Processing

Abstract

Structured Occurrence Nets (SONs) are a Petri net-based formalism designed to represent the behaviour of complex evolving systems, capturing concurrent events and interactions between subsystems. Recently, the modelling and visualisation of crime and cybercrime investigations have gained increasing interest. In particular, SONs have proven to be versatile tools for modelling and visualising various applications, including crime and cybercrime. This thesis presents two contributions aimed at making SON-based techniques suitable for real-life applications.

The main contribution is motivated by the fact that manually developing SON models from unstructured text can be time-consuming, as it requires extensive reading, comprehension, and model construction. This thesis aims to develop a methodology for the formal representation of unstructured textual resources in English. This involves experimenting, mapping, and deriving relationships between natural and formal languages, specifically using SON for crime modelling and visualisation as an application.

The second contribution addresses the scalability of SON-based representations for cybercrime analysis. It provides a novel approach in which acyclic nets have been extended with coloured features to enable reduction of net size to help in visualisation.

While the two contributions address distinct challenges, they are unified by their use of SONs as a formalism to model complex systems. Structured occurrence nets demonstrated their adaptability in representing both crime scenarios and cybercrime activities.