Trustworthiness evaluation of workers in critical facilities using electroencephalography-based acquaintance test

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. • A method to evaluate workers' trustworthiness in critical facilities using brain wave-based acquaintance tests is proposed. • The tests may provide an indication of behavior that disregards security policies and procedures with short measurement time. • Using biosignal to enhance security culture and mitigate insider threats in critical facilities may be possible.

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Publication Details

Journal
Nuclear Engineering and Technology
Published
2024-10-16
DOI
https://doi.org/10.1016/j.net.2024.10.019
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
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article

Trustworthiness evaluation of workers in critical facilities using electroencephalography-based acquaintance test

T. Kim, Man‐Sung Yim, Chul Min Kim
Nuclear Engineering and Technology
EEG and Brain-Computer Interfaces
article

Trustworthiness evaluation of workers in critical facilities using electroencephalography-based acquaintance test

T. Kim, Man‐Sung Yim, Chul Min Kim
article en

Abstract

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. • A method to evaluate workers' trustworthiness in critical facilities using brain wave-based acquaintance tests is proposed. • The tests may provide an indication of behavior that disregards security policies and procedures with short measurement time. • Using biosignal to enhance security culture and mitigate insider threats in critical facilities may be possible.

Nuclear Engineering and TechnologyVol. 57(3)
Korea Advanced Institute of Science and Technology (KR)
Decent work and economic growth
Openalex Percentile: Top 82%
EEG and Brain-Computer Interfaces
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