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Leveraging human thinking style for user attribution in digital forensic process

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Název: Leveraging human thinking style for user attribution in digital forensic process
Autoři: Adeyemi, Ikuesan Richard, Abd Razak, Shukor, Salleh, Mazleena, Venter, H.S. (Hein)
Informace o vydavateli: Indonesian Society for Knowledge and Human Development
Rok vydání: 2017
Sbírka: University of Pretoria: UPSpace
Témata: Sternberg thinking style, Online digital-signature, User attribution, Online user identification, Digital forensic process, Human thinking style
Popis: 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. ; Universiti Teknologi Malaysia and Ministry of Higher Education Malaysia under the vote number: R.J130000.7813.4F804. ; http://ijaseit.insightsociety.org ; am2018 ; Computer Science
Druh dokumentu: article in journal/newspaper
Popis souboru: application/pdf
Jazyk: English
Relation: http://hdl.handle.net/2263/65805
Dostupnost: http://hdl.handle.net/2263/65805
Rights: Article iis licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Přístupové číslo: edsbas.3975E3E3
Databáze: BASE
Popis
Abstrakt: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. ; Universiti Teknologi Malaysia and Ministry of Higher Education Malaysia under the vote number: R.J130000.7813.4F804. ; http://ijaseit.insightsociety.org ; am2018 ; Computer Science