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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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 20; no. 21; p. 6365
Main Authors: Kim, Jung Hwan, Kim, Chul Min, Yim, Man-Sung
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 08.11.2020
MDPI
Subjects:
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 Access:Get full text
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Summary: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 a malicious intention under scenarios of being forced to become an insider threat. The model also distinguishes insider threat scenarios from everyday conflict scenarios. To support model development, 21-channel EEG signals were measured on 25 healthy subjects, and sets of features were extracted from the time, time–frequency, frequency and nonlinear domains. To select the best use of the available features, automatic selection was performed by random-forest-based algorithms. The k-nearest neighbor, support vector machine with radial kernel, naïve Bayes, and multilayer perceptron algorithms were applied for the classification. By using EEG signals obtained while contemplating becoming an insider threat, the subject-wise model identified malicious intentions with 78.57% accuracy. The model also distinguished insider threat scenarios from everyday conflict scenarios with 93.47% accuracy. These findings could be utilized to support the development of insider threat mitigation systems along with existing trustworthiness assessments in the nuclear industry.
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These authors contributed equally.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20216365