Abstract:
User attribution, the process of identifying a human in a digital medium, is a research area that has received significant
attention in information security research areas, with a little research focus on digital forensics. This study explored the probability of
the existence of a digital fingerprint based on human thinking style, which can be used to identify an online user. To achieve this, the
study utilized Server-side web data of 43-respondents were collected for 10-months as well as a self-report thinking style
measurement instrument. Cluster dichotomies from five thinking styles were extracted. Supervised machine-learning techniques were
then applied to distinguish individuals on each dichotomy. The result showed that thinking styles of individuals on different
dichotomies could be reliably distinguished on the Internet using a Meta classifier of Logistic model tree with bagging technique. The
study further modelled how the observed signature can be adopted for a digital forensic process, using high-level universal modelling
language modelling process- specifically, the behavioural state-model and use-case modelling process. In addition to the application of
this result in forensics process, this result finds relevance and application in human-centered graphical user interface design for
recommender system as well as in e-commerce services. It also finds application in online profiling processes, especially in e-learning
systems.
