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|Title:||Unified psycholinguistic framework : an unobtrusive psychological analysis approach towards insider threat prevention and detection||Authors:||Na, Jin-Cheon
|Issue Date:||2019||Source:||Tan, S.-S., Na, J.-C., & Duraisamy, S. (2019). Unified psycholinguistic framework : an unobtrusive psychological analysis approach towards insider threat prevention and detection. Journal of Information Science Theory and Practice, 7(1), 52-71. doi:10.1633/JISTaP.2019.7.1.5||Series/Report no.:||Journal of Information Science Theory and Practice||Abstract:||An insider threat is a threat that comes from people within the organization being attacked. It can be described as a function of the motivation, opportunity, and capability of the insider. Compared to managing the dimensions of opportunity and capability, assessing one's motivation in committing malicious acts poses more challenges to organizations because it usually involves a more obtrusive process of psychological examination. The existing body of research in psycholinguistics suggests that automated text analysis of electronic communications can be an alternative for predicting and detecting insider threat through unobtrusive behavior monitoring. However, a major challenge in employing this approach is that it is difficult to minimize the risk of missing any potential threat while maintaining an acceptable false alarm rate. To deal with the trade-off between the risk of missed catches and the false alarm rate, we propose a unified psycholinguistic framework that consolidates multiple text analyzers to carry out sentiment analysis, emotion analysis, and topic modeling on electronic communications for unobtrusive psychological assessment. The user scenarios presented in this paper demonstrated how the trade-off issue can be attenuated with different text analyzers working collaboratively to provide more comprehensive summaries of users' psychological states.||URI:||https://hdl.handle.net/10356/100748
|ISSN:||2287-9099||DOI:||http://dx.doi.org/10.1633/JISTaP.2019.7.1.5||Rights:||© 2019 Sang-Sang Tan, Jin-Cheon Na, Santhiya Duraisamy. All JISTaP content is Open Access, meaning it is accessible online to everyone, without fee and authors’ permission. All JISTaP content is published and distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). Under this license, authors reserve the copyright for their content; however, they permit anyone to unrestrictedly use, distribute, and reproduce the content in any medium as far as the original authors and source are cited. For any reuse, redistribution, or reproduction of a work, users must clarify the license terms under which the work was produced.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||WKWSCI Journal Articles|
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