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...
Gespeichert in:
| Veröffentlicht in: | Sensors (Basel, Switzerland) Jg. 20; H. 21; S. 6365 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Switzerland
MDPI AG
08.11.2020
MDPI |
| Schlagworte: | |
| ISSN: | 1424-8220, 1424-8220 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | 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. |
|---|---|
| AbstractList | 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.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. 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. |
| Author | Kim, Chul Min Kim, Jung Hwan Yim, Man-Sung |
| AuthorAffiliation | Korea Advanced Institute of Science and Technology, Department of Nuclear and Quantum Engineering, Daejeon 34141, Korea; poxc@kaist.ac.kr (J.H.K.); usekim00@kaist.ac.kr (C.M.K.) |
| AuthorAffiliation_xml | – name: Korea Advanced Institute of Science and Technology, Department of Nuclear and Quantum Engineering, Daejeon 34141, Korea; poxc@kaist.ac.kr (J.H.K.); usekim00@kaist.ac.kr (C.M.K.) |
| Author_xml | – sequence: 1 givenname: Jung Hwan orcidid: 0000-0001-6903-6771 surname: Kim fullname: Kim, Jung Hwan – sequence: 2 givenname: Chul Min orcidid: 0000-0002-7405-6600 surname: Kim fullname: Kim, Chul Min – sequence: 3 givenname: Man-Sung surname: Yim fullname: Yim, Man-Sung |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33171609$$D View this record in MEDLINE/PubMed |
| BookMark | eNplkktPGzEQx62KqjzaQ79AtVIv7SHF7_VeKkEUaCQQlYCzNX4FR5s1tTdI_fY1CURATx7N_OY_45k5RHtDGjxCnwn-wViHjwvFlEgmxTt0QDjlE0Up3nth76PDUpYYU8aY-oD2GSMtkbg7QL9PhmY-PPgyxgWMMQ1NCtVRovO5ubnLHsbmMu6Cp1C8a6oxm50313ExQN9Meyglhmg3yEf0PkBf_Ken9wjdns1upr8mF1fn8-nJxcRy2Y0TZgG3mDDjjVMuEOY7I1vLwUCrDHggRvDQVXeQVFBhW6Kcs0aAwMB8YEdovtV1CZb6PscV5L86QdQbR8oLDXmMtvcagnGcEwHOYC5rRaxaElSHlTdGelu1fm617tdm5Z31w5ihfyX6OjLEO71ID7qVkkulqsC3J4Gc_qzrMPUqFuv7Hgaf1kVTLrr6DcZERb--QZdpnescKyUE5rTljFTqy8uOdq08L64Cx1vA5lRK9kHbOG4WUBuMvSZYP56G3p1Gzfj-JuNZ9H_2H7sYuJw |
| CitedBy_id | crossref_primary_10_3390_su14042163 crossref_primary_10_1080_13803395_2024_2364403 crossref_primary_10_1109_ACCESS_2021_3078470 crossref_primary_10_3389_fncom_2020_596531 crossref_primary_10_1016_j_trf_2025_02_025 crossref_primary_10_3390_s23031255 crossref_primary_10_1080_00295450_2020_1837583 |
| Cites_doi | 10.1109/TCYB.2015.2479240 10.1016/j.patrec.2015.06.013 10.1016/j.jneumeth.2003.10.009 10.1016/0013-4694(70)90143-4 10.1016/j.anucene.2017.05.006 10.1016/j.neuroimage.2009.02.006 10.1109/ROSE.2014.6952983 10.1080/00036810500130539 10.1109/ACCESS.2018.2857450 10.1109/JSYST.2015.2424677 10.1109/ICENCO.2016.7856467 10.1016/j.pnucene.2017.08.006 10.1016/j.neunet.2020.06.018 10.1109/TNSRE.2018.2850308 10.1016/j.anucene.2017.11.030 10.1145/2808783.2808792 10.1109/HICSS.2012.309 10.1117/12.820300 10.3389/fnhum.2019.00063 10.1080/00295450.2020.1837583 10.1086/209170 10.1145/3139923.3139930 10.2172/1452870 10.1109/JTEHM.2016.2609927 10.1080/17470919.2015.1059362 10.7591/9781501705946 10.1007/978-3-642-69746-3_2 10.1109/SPW.2018.00035 10.3389/fnagi.2018.00184 10.1109/CCWC.2018.8301639 10.1145/2659651.2659654 10.1016/j.seizure.2015.01.012 10.1007/s10916-008-9231-z 10.1109/IJCNN48605.2020.9207280 10.1007/978-3-642-02937-0 10.1016/j.neuron.2006.03.015 10.3389/fnins.2014.00376 10.1186/1471-2105-8-328 |
| ContentType | Journal Article |
| Copyright | 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2020 by the authors. 2020 |
| Copyright_xml | – notice: 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2020 by the authors. 2020 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.3390/s20216365 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni Edition) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) Medical Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1424-8220 |
| ExternalDocumentID | oai_doaj_org_article_afbd4415adb046beb0871f8908ebb6ec PMC7664688 33171609 10_3390_s20216365 |
| Genre | Journal Article |
| GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M 3V. ABJCF ALIPV ARAPS CGR CUY CVF ECM EIF HCIFZ KB. M7S NPM PDBOC 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c469t-3ca07013bebd8df13e9b67c4aba78baea1b54f93e9f62525c718ddcb5a50a3ef3 |
| IEDL.DBID | BENPR |
| ISICitedReferencesCount | 10 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000593550600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1424-8220 |
| IngestDate | Tue Oct 14 19:08:40 EDT 2025 Tue Nov 04 01:57:22 EST 2025 Fri Sep 05 11:06:22 EDT 2025 Tue Oct 07 07:27:59 EDT 2025 Wed Feb 19 02:27:57 EST 2025 Tue Nov 18 22:21:04 EST 2025 Sat Nov 29 07:10:30 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 21 |
| Keywords | electroencephalography insider threat nuclear security implicit intention machine learning subject-wise classification |
| Language | English |
| License | Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c469t-3ca07013bebd8df13e9b67c4aba78baea1b54f93e9f62525c718ddcb5a50a3ef3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 These authors contributed equally. |
| ORCID | 0000-0001-6903-6771 0000-0002-7405-6600 |
| OpenAccessLink | https://www.proquest.com/docview/2550427431?pq-origsite=%requestingapplication% |
| PMID | 33171609 |
| PQID | 2550427431 |
| PQPubID | 2032333 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_afbd4415adb046beb0871f8908ebb6ec pubmedcentral_primary_oai_pubmedcentral_nih_gov_7664688 proquest_miscellaneous_2459625335 proquest_journals_2550427431 pubmed_primary_33171609 crossref_citationtrail_10_3390_s20216365 crossref_primary_10_3390_s20216365 |
| PublicationCentury | 2000 |
| PublicationDate | 20201108 |
| PublicationDateYYYYMMDD | 2020-11-08 |
| PublicationDate_xml | – month: 11 year: 2020 text: 20201108 day: 8 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland – name: Basel |
| PublicationTitle | Sensors (Basel, Switzerland) |
| PublicationTitleAlternate | Sensors (Basel) |
| PublicationYear | 2020 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Hjorth (ref_43) 1970; 29 ref_14 ref_58 ref_57 ref_12 ref_56 ref_11 Deng (ref_33) 2018; 26 Subha (ref_42) 2010; 34 ref_55 ref_10 ref_54 ref_51 ref_19 ref_18 ref_17 (ref_47) 2015; 9 Kim (ref_31) 2019; 13 ref_25 ref_24 ref_22 ref_20 ref_27 Kim (ref_6) 2017; 108 Sheppard (ref_37) 1988; 15 Bulea (ref_29) 2014; 8 Koessler (ref_53) 2009; 46 Suh (ref_15) 2018; 113 Hunker (ref_13) 2011; 2 Shah (ref_34) 2020; 130 Almehmadi (ref_23) 2018; 60 ref_36 ref_35 Delorme (ref_39) 2004; 134 Almehmadi (ref_16) 2015; 11 ref_30 Faust (ref_46) 2015; 26 Dong (ref_26) 2015; 46 Dong (ref_50) 2016; 11 Goldberg (ref_52) 2006; 50 Zhang (ref_21) 2005; 22 ref_45 Zou (ref_7) 2018; 104 ref_44 ref_41 Joe (ref_38) 2000; 8 ref_40 ref_1 ref_3 Zoubi (ref_32) 2018; 10 ref_2 ref_49 ref_48 ref_9 ref_8 ref_5 ref_4 Kang (ref_28) 2015; 66 |
| References_xml | – volume: 46 start-page: 2535 year: 2015 ident: ref_26 article-title: EEG-based classification of implicit intention during self-relevant sentence reading publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2015.2479240 – volume: 66 start-page: 144 year: 2015 ident: ref_28 article-title: Human implicit intent recognition based on the phase synchrony of EEG signals publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2015.06.013 – volume: 134 start-page: 9 year: 2004 ident: ref_39 article-title: EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2003.10.009 – volume: 29 start-page: 306 year: 1970 ident: ref_43 article-title: EEG analysis based on time domain properties publication-title: Electroencephalogr. Clin. Neurophysiol. doi: 10.1016/0013-4694(70)90143-4 – ident: ref_9 – ident: ref_5 – volume: 2 start-page: 4 year: 2011 ident: ref_13 article-title: Insiders and insider threats-an overview of definitions and mitigation techniques publication-title: J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. – volume: 108 start-page: 301 year: 2017 ident: ref_6 article-title: A study of insider threat in nuclear security analysis using game theoretic modeling publication-title: Ann. Nucl. Energy doi: 10.1016/j.anucene.2017.05.006 – volume: 8 start-page: 72 year: 2000 ident: ref_38 article-title: Standardization of Korean version of daily stress inventory (K-DSI) publication-title: Korean J. Psychosom. Med. – volume: 46 start-page: 64 year: 2009 ident: ref_53 article-title: Automated cortical projection of EEG sensors: Anatomical correlation via the international 10–10 system publication-title: NeuroImage doi: 10.1016/j.neuroimage.2009.02.006 – ident: ref_1 – ident: ref_44 doi: 10.1109/ROSE.2014.6952983 – volume: 22 start-page: 107 year: 2005 ident: ref_21 article-title: Harmony, hierarchy and conservatism: A cross-cultural comparison of Confucian values in China, Korea, Japan, and Taiwan publication-title: Commun. Res. Rep. doi: 10.1080/00036810500130539 – volume: 60 start-page: 40626 year: 2018 ident: ref_23 article-title: Micromovement behavior as an intention detection measurement for preventing insider threats publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2857450 – volume: 11 start-page: 373 year: 2015 ident: ref_16 article-title: On the possibility of insider threat prevention using intent-based access control (IBAC) publication-title: IEEE Syst. J. doi: 10.1109/JSYST.2015.2424677 – ident: ref_8 – ident: ref_4 – ident: ref_45 doi: 10.1109/ICENCO.2016.7856467 – volume: 104 start-page: 8 year: 2018 ident: ref_7 article-title: Insider threats of physical protection systems in nuclear power plants: Prevention and evaluation publication-title: Prog. Nucl. Energy doi: 10.1016/j.pnucene.2017.08.006 – volume: 130 start-page: 75 year: 2020 ident: ref_34 article-title: Dynamical system based compact deep hybrid network for classification of Parkinson disease related EEG signals publication-title: Neural Netw. doi: 10.1016/j.neunet.2020.06.018 – ident: ref_27 – volume: 26 start-page: 1481 year: 2018 ident: ref_33 article-title: Transductive joint-knowledge-transfer TSK FS for recognition of epileptic EEG signals publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2018.2850308 – ident: ref_10 – volume: 113 start-page: 308 year: 2018 ident: ref_15 article-title: “High risk non-initiating insider” identification based on EEG analysis for enhancing nuclear security publication-title: Ann. Nucl. Energy doi: 10.1016/j.anucene.2017.11.030 – ident: ref_24 doi: 10.1145/2808783.2808792 – volume: 9 start-page: 2309 year: 2015 ident: ref_47 article-title: Analysis of EEG signals using nonlinear dynamics and chaos: A review publication-title: Appl. Math. Inf. Sci. – ident: ref_41 – ident: ref_17 – ident: ref_14 doi: 10.1109/HICSS.2012.309 – ident: ref_57 doi: 10.1117/12.820300 – ident: ref_30 – volume: 13 start-page: 63 year: 2019 ident: ref_31 article-title: Classification of movement intention using independent components of premovement EEG publication-title: Front. Hum. Neurosci. doi: 10.3389/fnhum.2019.00063 – ident: ref_58 doi: 10.1080/00295450.2020.1837583 – ident: ref_3 – ident: ref_11 – volume: 15 start-page: 325 year: 1988 ident: ref_37 article-title: The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research publication-title: J. Consum. Res. doi: 10.1086/209170 – ident: ref_51 doi: 10.1145/3139923.3139930 – ident: ref_40 – ident: ref_25 doi: 10.2172/1452870 – ident: ref_55 doi: 10.1109/JTEHM.2016.2609927 – volume: 11 start-page: 221 year: 2016 ident: ref_50 article-title: Implicit agreeing/disagreeing intention while reading self-relevant sentences: A human fMRI study publication-title: Soc. Neurosci. doi: 10.1080/17470919.2015.1059362 – ident: ref_18 – ident: ref_20 doi: 10.7591/9781501705946 – ident: ref_36 doi: 10.1007/978-3-642-69746-3_2 – ident: ref_12 doi: 10.1109/SPW.2018.00035 – volume: 10 start-page: 184 year: 2018 ident: ref_32 article-title: Predicting age from brain EEG signals—A machine learning approach publication-title: Front. Aging Neurosci. doi: 10.3389/fnagi.2018.00184 – ident: ref_56 doi: 10.1109/CCWC.2018.8301639 – ident: ref_54 – ident: ref_2 – ident: ref_22 doi: 10.1145/2659651.2659654 – volume: 26 start-page: 56 year: 2015 ident: ref_46 article-title: Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis publication-title: Seizure doi: 10.1016/j.seizure.2015.01.012 – volume: 34 start-page: 195 year: 2010 ident: ref_42 article-title: EEG signal analysis: A survey publication-title: J. Med. Syst. doi: 10.1007/s10916-008-9231-z – ident: ref_35 doi: 10.1109/IJCNN48605.2020.9207280 – ident: ref_49 doi: 10.1007/978-3-642-02937-0 – volume: 50 start-page: 329 year: 2006 ident: ref_52 article-title: When the brain loses its self: Prefrontal inactivation during sensorimotor processing publication-title: Neuron doi: 10.1016/j.neuron.2006.03.015 – ident: ref_19 – volume: 8 start-page: 376 year: 2014 ident: ref_29 article-title: Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution publication-title: Front. Neurosci. doi: 10.3389/fnins.2014.00376 – ident: ref_48 doi: 10.1186/1471-2105-8-328 |
| SSID | ssj0023338 |
| Score | 2.396468 |
| Snippet | This study proposes a scheme to identify insider threats in nuclear facilities through the detection of malicious intentions of potential insiders using... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 6365 |
| SubjectTerms | 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 |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1NS-RAEC1E9qCHxe_NOitx8eAlmEknnc7RWcbdiyLogrdQ_aUDS0accX-_VZ1MyIiwl72F7iJ0qlKp95LOK4AzW-qS_7lMrCzyJFfaJBUSkFOVsV6k6KVXodlEeXOjHh6q20GrL94T1soDt467QK8tY360mqicdjoliO9VlSqntXSGn76EelZkqqNagphXqyMkiNRfLIjiE_DgCjKoPkGk_yNk-X6D5KDiXO3A5w4qxpftEndhwzV7sD0QENyH28smHihlzJt47mkgtOCM758YEMbXs35yQiXLxnQwnf6M72aPfPrQFZP3CwWTA_h9Nb3_8SvpeiQkhojtMhEGKWnHghxjlfVj4SotS5OjxlJpdDjWRe4rGvbEdLLCUC2y1ugCixSF8-IQNpt5475ALDBH9GnmtMly6RGlKWVhZVZRYjufRXC-8l1tOgFx7mPxpyYiwW6uezdH8L03fW5VMz4ymnAAegMWug4DFP66C3_9r_BHMFqFr-6yb1ETTeIWIoSNIjjtpylv-GMINm7-SjY59x0isEvrOGqj3a9ECBYRSqsIyrX7YG2p6zPN7Cloc5dS5lKpr__j2o5hK2N2H15ij2Bz-fLqvsEn83c5W7ychBv-DYkACZw priority: 102 providerName: Directory of Open Access Journals |
| Title | An Investigation of Insider Threat Mitigation Based on EEG Signal Classification |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/33171609 https://www.proquest.com/docview/2550427431 https://www.proquest.com/docview/2459625335 https://pubmed.ncbi.nlm.nih.gov/PMC7664688 https://doaj.org/article/afbd4415adb046beb0871f8908ebb6ec |
| Volume | 20 |
| WOSCitedRecordID | wos000593550600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: Directory of Open Access Journals customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9NAEB7RlAMcKK-CoUQGceBi1fGud9cn1FQucEhkQZHCydpnGwnZJUk58tuZ3ThuUlVcuFjW7sgae3Y8D6-_D-C94Yr7fy4Tw3KaUKF0UkhM5EShjSOpdMyJQDbBp1MxmxVV13BbdtsqN-_E8KI2rfY98mNMfT0tBMa7j1e_Es8a5b-udhQae7DvkcroAPbH5bT62pdcBCuwNZ4QweL-eImlPiYgPpJsRaEA1n9Xhnl7o-RW5Dk7-F-dH8OjLueMT9aL5Ancs81TeLiFRPgMqpMm3oLcaJu4dTgQuDzj80ufWcaTeT85xthnYjwpy0_xt_mFv3yg1_Qbj4LIc_h-Vp6ffk46soVEY4W8SoiW6P0joqwywrgRsYViXFOpJBdKWjlSOXUFDjssmbJcY1AzRqtc5qkk1pFDGDRtY19CTCSV0qWZVTqjzEnJNGe5YVmBT8O6LIIPm4df6w6J3BNi_KyxIvF2qns7RfCuF71aw2_cJTT2FuwFPGJ2GGgXF3XngLV0yvjaURqVUoZ3mWKp6ESRCqsUszqCo40N686Nl_WNASN420-jA_qvKrKx7TXKUE9ghFkz6vFivVx6TQjxaERpEQHfWUg7qu7ONPPLAPLNGaNMiFf_Vus1PMh8AyD0uY9gsFpc2zdwX_9ezZeLIezxGQ9HMew8YxiaDnic_ClxrPoyqX78BfCpHQY |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLRJw4P0IFAgIJC5Rs7Hj2AeEWtjSVburlVik9hT8bFdCSdndgvhT_EbGebGLKm49cIvskeUkn2fmc5z5AF6ZTGX-n8vIsJRGlCsdCYmJHBfaOBJLxxyvxCay8ZgfHYnJBvxq_4Xxxypbn1g5alNqv0e-jamvl4XAePfu7FvkVaP819VWQqOGxYH9-QMp2-Lt8AO-39dJsjeYvt-PGlWBSCMVXEZES4R5nyirDDeuT6xQLNNUKplxJa3sq5Q6gc0OuUGSavTexmiVyjSWxDqC416BTYpg5z3YnAxHk-OO4hFkfHX9IkJEvL1IMIQy4iPXStSrxAEuymj_Ppi5Eun2bv1vz-g23Gxy6nCnXgR3YMMWd-HGSqXFezDZKcKVkiJlEZYOGyqt0nB66jPncDTrOncxtpsQLwaDj-Gn2YkfvpIP9QerKpP78PlSbukB9IqysI8gJJJK6eLEKp1Q5qRkOmOpYYnAp29dEsCb9mXnuqm07gU_vubIuDwu8g4XAbzsTM_q8iIXGe16xHQGviJ41VDOT_LGweTSKeO5sTQqpgzvMkYq7LiIuVWKWR3AVouZvHFTi_wPYAJ40XWjg_FfjWRhy3O0oV6gCVkBzuNhDc9uJoT4akuxCCBbA-7aVNd7itlpVcQ8Y4wyzh__e1rP4dr-dHSYHw7HB0_geuI3O6o9_S3oLefn9ilc1d-Xs8X8WbMSQ_hy2cD-DXvTeAM |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VFiE48H4EChgEEpdos7HjOAeEWroLq9JVJIpUTsHPdiWUlN0tiL_Gr2OcF7uo4tYDt8geRU78eWY-x5kP4IVJVer_uQwNT1jIhNJhJjGRE5k2jkbScSdqsYl0OhVHR1m-Ab-6f2H8scrOJ9aO2lTa75EPMPX1shAY7wauPRaR743fnH4LvYKU_9LayWk0ENm3P38gfVu8nuzhXL-M4_Ho8O37sFUYCDXSwmVItUTID6myygjjhtRmiqeaSSVToaSVQ5Uwl2GzQ54QJxo9uTFaJTKJJLWO4n0vwRam5AzX2FY-Ocg_93SPIvtrahlRmkWDRYzhlFMfxVYiYC0UcF52-_chzZWoN77xP7-vm3C9zbXJTrM4bsGGLW_DtZUKjHcg3ynJSqmRqiSVw4Zaw5QcnviMmhzM-s5djPmG4MVo9I58nB3729eyov7AVW1yFz5dyCPdg82yKu0DIFQyKV0UW6Vjxp2UXKc8MTzOcCasiwN41U18odsK7F4I5GuBTMxjpOgxEsDz3vS0KTtyntGuR09v4CuF1w3V_LhoHU8hnTKeM0ujIsbxKSOkyE5kkbBKcasD2O7wU7Tua1H8AU8Az_pudDz-a5IsbXWGNswLNyFbwHHcb6Daj4RSX4UpygJI10C8NtT1nnJ2Uhc3TzlnXIiH_x7WU7iCaC4-TKb7j-Bq7PdA6q3-bdhczs_sY7isvy9ni_mTdlES-HLRuP4NrZCAww |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=An+Investigation+of+Insider+Threat+Mitigation+Based+on+EEG+Signal+Classification&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Kim%2C+Jung+Hwan&rft.au=Kim%2C+Chul+Min&rft.au=Yim%2C+Man-Sung&rft.date=2020-11-08&rft.eissn=1424-8220&rft.volume=20&rft.issue=21&rft_id=info:doi/10.3390%2Fs20216365&rft_id=info%3Apmid%2F33171609&rft.externalDocID=33171609 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon |