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Leveraging Human Thinking Style for User Attribution in Digital Forensic Process

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@article{IJASEIT1383,
   author = {Adeyemi Richard Ikuesan and Shukor Abd Razak and Mazleena Salleh and Hein S. Venter},
   title = {Leveraging Human Thinking Style for User Attribution in Digital Forensic Process},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {1},
   year = {2017},
   pages = {198--206},
   keywords = {Sternberg thinking style; online digital-signature; User attribution; online user identification; digital forensic process.},
   abstract = {User attribution, the process of identifying a human in a digital medium, is a research area that has receive 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 modeled how the observed signature can be adopted for a digital forensic process, using high-level universal modeling language modeling process- specifically, the behavioral state-model and use-case modeling 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},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1383},
   doi = {10.18517/ijaseit.7.1.1383}
}

EndNote

%A Ikuesan, Adeyemi Richard
%A Abd Razak, Shukor
%A Salleh, Mazleena
%A Venter, Hein S.
%D 2017
%T Leveraging Human Thinking Style for User Attribution in Digital Forensic Process
%B 2017
%9 Sternberg thinking style; online digital-signature; User attribution; online user identification; digital forensic process.
%! Leveraging Human Thinking Style for User Attribution in Digital Forensic Process
%K Sternberg thinking style; online digital-signature; User attribution; online user identification; digital forensic process.
%X User attribution, the process of identifying a human in a digital medium, is a research area that has receive 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 modeled how the observed signature can be adopted for a digital forensic process, using high-level universal modeling language modeling process- specifically, the behavioral state-model and use-case modeling 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
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1383
%R doi:10.18517/ijaseit.7.1.1383
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 1
%@ 2088-5334

IEEE

Adeyemi Richard Ikuesan,Shukor Abd Razak,Mazleena Salleh and Hein S. Venter,"Leveraging Human Thinking Style for User Attribution in Digital Forensic Process," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 1, pp. 198-206, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.1.1383.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Ikuesan, Adeyemi Richard
AU  - Abd Razak, Shukor
AU  - Salleh, Mazleena
AU  - Venter, Hein S.
PY  - 2017
TI  - Leveraging Human Thinking Style for User Attribution in Digital Forensic Process
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 1
Y2  - 2017
SP  - 198
EP  - 206
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Sternberg thinking style; online digital-signature; User attribution; online user identification; digital forensic process.
N2  - User attribution, the process of identifying a human in a digital medium, is a research area that has receive 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 modeled how the observed signature can be adopted for a digital forensic process, using high-level universal modeling language modeling process- specifically, the behavioral state-model and use-case modeling 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
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1383
DO  - 10.18517/ijaseit.7.1.1383

RefWorks

RT Journal Article
ID 1383
A1 Ikuesan, Adeyemi Richard
A1 Abd Razak, Shukor
A1 Salleh, Mazleena
A1 Venter, Hein S.
T1 Leveraging Human Thinking Style for User Attribution in Digital Forensic Process
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 1
YR 2017
SP 198
OP 206
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Sternberg thinking style; online digital-signature; User attribution; online user identification; digital forensic process.
AB User attribution, the process of identifying a human in a digital medium, is a research area that has receive 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 modeled how the observed signature can be adopted for a digital forensic process, using high-level universal modeling language modeling process- specifically, the behavioral state-model and use-case modeling 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
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1383
DO  - 10.18517/ijaseit.7.1.1383