Analysing and visualising data sets of cybercrime investigations using Structured Occurrence Nets

Abstract

Structured Occurrence Nets (SONs) are a Petri net based formalism for portraying the behaviour of complex evolving systems. As a concept, SONs are derived from Occurrence Nets (ONs). SONs provide a powerful framework for evolving system analysis and are supported by the existing SONCraft toolset. On the other hand, modelling of cybercrime investigations has become of interest in recent years, and large-scale criminal investigations have been considered as complex evolving systems. Right now, they present a significant challenge for police investigators and analysts. The current thesis contributes to addressing this challenge in two different ways: (i) by presenting an algorithm and an implemented tool that visualise data sets using maximal concurrency; and (ii) by detecting DNS tunnelling through a novel SON-based technique and tool. Moreover, the theoretical contribution of this thesis focuses on model extensions and abstraction; in particular, it introduces a new class of SONs based on multi-coloured tokens.