This study emphasized the critical importance of prioritizing workers' trustworthiness in safeguarding critical facilities from the potential harm caused by insiders' betrayal. By noting the limitations of existing physical protection systems in critical facilities, this study showed that effectively characterizing and detecting malicious insider activity can be possible by observing immediate brain physiological reactions to specific stimuli. Based on the finding, a novel approach using electroencephalography (EEG)-based acquaintance tests was introduced to objectively assess ’a level of suspicion between colleagues by measuring their brain responses when he/she is exposed to familiar and unfamiliar stimuli. If a person claims falsely about one's acquaintance, the model assumes he/she has malicious or suspicious intent by violating the reporting obligation. The experiment was designed with a relatively short time of monitoring of less than 2 min and with a simple EEG headband device called MUSE. The experiment is to analyze whether the model could provide reliable prediction of disguised acquaintance avoiding complex preparation process. Averaged N170 peak analysis indicated that MUSE provided adequate signal characteristics to classify acquaintance. Also, a machine learning-based subject-wise classification model showed adequate capability to differentiate the EEG signals of acquaintances from unknowns. The final prediction combined multiple single-trial classification results, correctly detected the participant's acquaintance about 94.1 % of the cases, with similar performance when strangers were presented. The results indicated the possibility of using biosignal to enhance security culture and mitigate insider threats in critical facilities, by providing an indication of behavior that disregards security policies and procedures.
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