An Investigation of Insider Threat Mitigation Based on EEG Signal Classification

Kim, Junghwan; Kim, Chul Min; Yim, Man‐Sung

doi:10.3390/s20216365

Cited by 14 publications

(4 citation statements)

References 38 publications

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“…In this study, use of all the available EEG channels and their potentially associated features were not considered under the consideration of avoiding overfitting of the machine learning algorithm. In fact, taking economic and ergonomic aspects into consideration, recording a full set of EEG data may not be desirable (Kim et al, 2020a ). To examine this point, this study compared the EEG results from the total brain (21 channels) to the BA 10 (three channels: Fp1, Fp2, and Fpz).…”

Section: Discussionmentioning

confidence: 99%

Biosignal-Based Attention Monitoring to Support Nuclear Operator Safety-Relevant Tasks

Kim

Jung

et al. 2020

Front. Comput. Neurosci.

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In the main control room (MCR) of a nuclear power plant (NPP), the quality of an operator's performance can depend on their level of attention to the task. Insufficient operator attention accounted for more than 26% of the total causes of human errors and is the highest category for errors. It is therefore necessary to check whether operators are sufficiently attentive either as supervisors or peers during reactor operation. Recently, digital control technologies have been introduced to the operating environment of an NPP MCR. These upgrades are expected to enhance plant and operator performance. At the same time, because personal computers are used in the advanced MCR, the operators perform more cognitive works than physical work. However, operators may not consciously check fellow operators' attention in this environment indicating potentially higher importance of the role of operator attention. Therefore, remote measurement of an operator's attention in real time would be a useful tool, providing feedback to supervisors. The objective of this study is to investigate the development of quantitative indicators that can identify an operator's attention, to diagnose or detect a lack of operator attention thus preventing potential human errors in advanced MCRs. To establish a robust baseline of operator attention, this study used two of the widely used biosignals: electroencephalography (EEG) and eye movement. We designed an experiment to collect EEG and eye movements of the subjects who were monitoring and diagnosing nuclear operator safety-relevant tasks. There was a statistically significant difference between biosignals with and without appropriate attention. Furthermore, an average classification accuracy of about 90% was obtained by the k-nearest neighbors and support vector machine classifiers with a few EEG and eye movements features. Potential applications of EEG and eye movement measures in monitoring and diagnosis tasks in an NPP MCR are also discussed.

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Section: Discussionmentioning

confidence: 99%

Biosignal-Based Attention Monitoring to Support Nuclear Operator Safety-Relevant Tasks

Kim

Jung

et al. 2020

Front. Comput. Neurosci.

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“…Our previous work involving EEG-based classification, features extracted from the time domain that were found significant [11]. Based on these results, this study selected features from the time domain of the EEG signals that supported the development of a deep learning algorithm.…”

Section: Feature Constructionmentioning

confidence: 97%

“…It may be possible to link a subject's EEG data to some of these PI attributes. Without adequate data privacy, ethical and legal issues could stifle the collection of EEG data and its use [11], [12]. Hence, EEG data and any related PI must be protected and treated as sensitive information.…”

Section: Introductionmentioning

confidence: 99%

Development of an Information Security-Enforced EEG-Based Nuclear Operators’ Fitness for Duty Classification System

Kim¹,

Cho²,

Suh³

et al. 2021

IEEE Access

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In a nuclear power plant (NPP), operator performance is a critical to ensure safe operation of the plant. The fitness for duty (FFD) of the operators should be systematically assessed before they engage in duties related to reactor operations. This study proposes the use of an electroencephalography (EEG)-based deep learning algorithm to classify an operator's FFD. To determine the suitability of this approach, EEG data were collected during simple cognitive exercises designed to examine the mental readiness of nuclear operators. The EEG-based FFD classification system designed could successfully determine an operator's sobriety, stress, and fatigue in a timely and cost-effective manner. As protecting personal information of the operators while using their EEG data is important and necessary, this study also investigated schemes for providing information security to the EEGbased FFD status classification system by following the International Organization for Standardization/International Electrotechnical Commission standard. Data confidentiality, integrity, and unlinkability were considered in the resulting schemes of information security for the EEG data. The resulting system provides the necessary protection of personal information and the FFD databases without significantly affecting the overhead of FFD classification through near real-time analysis.INDEX TERMS Information security, brain computer interface, electroencephalography (EEG), deep learning, nuclear safety, fitness for duty (FFD).

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“…Insiders can make use of their understanding of the loopholes in the physical protection system to help external enemies carry out malicious acts such as illegal transfer, theft of nuclear materials or destruction of nuclear facilities [4]. Most known nuclear security events were carried out by insiders or at least with the help of insiders [5]. It is speculated that the theft of highly enriched uranium in Russia and the destruction of nuclear power plants in Belgium, which caused significant economic losses, were carried out by unidentified insiders [6].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Game Model of Internal Threats to Nuclear Security in Spent Fuel Reprocessing Plants Based on RDEU Theory

Zou

Jia-hua³

2022

Sustainability

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The internal threat to nuclear security is one of the most serious problems in the physical protection supervision of spent fuel reprocessing plants. Both insiders and nuclear security departments have obvious characteristics of situational decision making and even irrational decision making. Combined with Game theory and RDEU theory, the RDEU Game model of insiders and nuclear security departments was constructed to analyze the existence of equilibrium solutions of two-way strategies under different emotional states. From a dynamic point of view, the influence and change process of emotion on participants’ decision-making behavior were analyzed. Then, the model was numerically simulated to verify its accuracy and effectiveness, which showed that different emotional states and intensities would not only affect the final result of evolutionary equilibrium, but also change the evolution speed of the strategies. In addition, compared with insiders, the intensity of pessimism in the nuclear security department had a greater impact on the game equilibrium. Finally, we present some reasonable recommendations to prevent and protect nuclear security events at spent fuel reprocessing plants by strengthening the emotional supervision and guidance of insiders and the nuclear security department.

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An Investigation of Insider Threat Mitigation Based on EEG Signal Classification

Cited by 14 publications

References 38 publications

Biosignal-Based Attention Monitoring to Support Nuclear Operator Safety-Relevant Tasks

Biosignal-Based Attention Monitoring to Support Nuclear Operator Safety-Relevant Tasks

Development of an Information Security-Enforced EEG-Based Nuclear Operators’ Fitness for Duty Classification System

Evolutionary Game Model of Internal Threats to Nuclear Security in Spent Fuel Reprocessing Plants Based on RDEU Theory

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