Leveraging human thinking style for user attribution in digital forensic process
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 fingerprin...
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| Main Authors: | , , , |
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| Format: | Article |
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Insight Society
2017
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| Subjects: | |
| Online Access: | http://eprints.utm.my/66172/ http://eprints.utm.my/66172/ http://eprints.utm.my/66172/ |
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| Summary: | 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. |
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