dc.contributor.authorJia, Xiaofan
dc.description.abstractAs information becomes increasingly digitalized, confidential data of an organization faces unprecedented challenges. The situation may be even worse when the attack is conducted by insiders. Thus, based on emotion and psychological data, this dissertation project aims to present a model which could predict whether an individual tends to be an Intellectual Property theft. In this report, the author firstly provides the motivation, background and scope of the project. Then, literature review is presented. After which, the procedure to process data, extract emotion feature and implement data visualization are elaborated in sequence. Specifically, graphs based on Spearman correlation show satisfying features. Additionally, four machine learning algorithms are applied for classification. Compared with other algorithms, the decision tree provides the best performance at present stage. Finally, the sixth chapter concludes the work and proposes several recommendations for future work.en_US
dc.format.extent62 p.en_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleGame assessment tool of IP theften_US
dc.contributor.supervisorJustin Dauwelsen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Electronics)en_US

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