An Investigation of Insider Threat Mitigation Based on EEG Signal Classification

This study proposes a scheme to identify insider threats in nuclear facilities through the detection of malicious intentions of potential insiders using subject-wise classification. Based on electroencephalography (EEG) signals, a classification model was developed to identify whether a subject has...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 20; H. 21; S. 6365
Hauptverfasser:	Kim, Jung Hwan, Kim, Chul Min, Yim, Man-Sung
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 08.11.2020 MDPI
Schlagworte:	Access control Algorithms Bayes Theorem Behavior Criminal investigations Cybersecurity Electroencephalography Employment Humans implicit intention insider threat machine learning Nuclear Power Plants Nuclear security Security systems Signal Processing, Computer-Assisted subject-wise classification Support Vector Machine Surveillance Terrorism Threats Workers electroencephalography insider threat nuclear security implicit intention machine learning subject-wise classification
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
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