{"id":"https://openalex.org/W1937965119","doi":"https://doi.org/10.1109/biosig.2015.7314612","title":"On Accuracy of Keystroke Authentications Based on Commonly Used English Words","display_name":"On Accuracy of Keystroke Authentications Based on Commonly Used English Words","publication_year":2015,"publication_date":"2015-09-01","ids":{"openalex":"https://openalex.org/W1937965119","doi":"https://doi.org/10.1109/biosig.2015.7314612","mag":"1937965119"},"language":"en","primary_location":{"id":"doi:10.1109/biosig.2015.7314612","is_oa":false,"landing_page_url":"https://doi.org/10.1109/biosig.2015.7314612","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference of the Biometrics Special Interest Group (BIOSIG)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079878803","display_name":"Alaa Darabseh","orcid":null},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alaa Darabseh","raw_affiliation_strings":["Department of Computer Science, Texas Tech University, Lubbock, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Texas Tech University, Lubbock, TX, USA","institution_ids":["https://openalex.org/I12315562"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026464816","display_name":"Akbar Siami Namin","orcid":"https://orcid.org/0000-0002-1646-7495"},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Akbar Siami Namin","raw_affiliation_strings":["Department of Computer Science, Texas Tech University, Lubbock, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Texas Tech University, Lubbock, TX, USA","institution_ids":["https://openalex.org/I12315562"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.08133482,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"1","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11800","display_name":"User Authentication and Security Systems","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11800","display_name":"User Authentication and Security Systems","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.9679999947547913,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9667999744415283,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/keystroke-logging","display_name":"Keystroke logging","score":0.8538259267807007},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.82427579164505},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7434406280517578},{"id":"https://openalex.org/keywords/keystroke-dynamics","display_name":"Keystroke dynamics","score":0.6961005330085754},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6352323889732361},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6030591726303101},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5111423134803772},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43534255027770996},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32982710003852844},{"id":"https://openalex.org/keywords/password","display_name":"Password","score":0.08279135823249817}],"concepts":[{"id":"https://openalex.org/C161615301","wikidata":"https://www.wikidata.org/wiki/Q309396","display_name":"Keystroke logging","level":2,"score":0.8538259267807007},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.82427579164505},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7434406280517578},{"id":"https://openalex.org/C79540074","wikidata":"https://www.wikidata.org/wiki/Q3269465","display_name":"Keystroke dynamics","level":4,"score":0.6961005330085754},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6352323889732361},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6030591726303101},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5111423134803772},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43534255027770996},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32982710003852844},{"id":"https://openalex.org/C109297577","wikidata":"https://www.wikidata.org/wiki/Q161157","display_name":"Password","level":2,"score":0.08279135823249817},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C4957475","wikidata":"https://www.wikidata.org/wiki/Q242186","display_name":"S/KEY","level":3,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/biosig.2015.7314612","is_oa":false,"landing_page_url":"https://doi.org/10.1109/biosig.2015.7314612","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference of the Biometrics Special Interest Group (BIOSIG)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.4300000071525574}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1584630980","https://openalex.org/W1981157808","https://openalex.org/W1983159352","https://openalex.org/W1992245152","https://openalex.org/W2100115367","https://openalex.org/W2157245832"],"related_works":["https://openalex.org/W2155670618","https://openalex.org/W2052279280","https://openalex.org/W2159333170","https://openalex.org/W2031617473","https://openalex.org/W1573560902","https://openalex.org/W4224061638","https://openalex.org/W4244802227","https://openalex.org/W2906096565","https://openalex.org/W2888741745","https://openalex.org/W3169822312"],"abstract_inverted_index":{"The":[0,51,110,136,145],"aim":[1],"of":[2,24,39,45,53,84,97],"this":[3,16],"research":[4],"is":[5],"to":[6,80],"advance":[7],"the":[8,20,37,82,91,94,154,178,181],"user":[9],"active":[10],"authentication":[11,31],"using":[12],"keystroke":[13,26,29,40,108,161],"dynamics.":[14],"Through":[15],"research,":[17],"we":[18,35],"assess":[19],"performance":[21,38,52,83,156],"and":[22,69,131,175],"influence":[23],"various":[25],"features":[27,41,55],"on":[28,42],"dynamics":[30],"systems.":[32],"In":[33],"particular,":[34],"investigate":[36],"a":[43],"subset":[44],"most":[46],"frequently":[47],"used":[48],"English":[49],"words.":[50],"four":[54,160,182],"such":[56],"as":[57,88,90],"i)":[58],"key":[59,150],"duration,":[60],"ii)":[61],"flight":[62],"time":[63,67,73,152],"latency,":[64,68],"iii)":[65],"digraph":[66],"iv)":[70],"word":[71,165],"total":[72,166],"duration":[74,151],"are":[75,78,104,114,140],"analyzed.":[76],"Experiments":[77],"performed":[79],"measure":[81],"each":[85],"feature":[86],"individually":[87],"well":[89],"results":[92,147,170],"from":[93],"different":[95],"subsets":[96],"these":[98],"features.":[99],"Four":[100],"machine":[101,118,124],"learning":[102],"techniques":[103],"employed":[105],"for":[106,142],"assessing":[107],"authentications.":[109],"selected":[111],"classification":[112],"methods":[113],"two-class":[115],"support":[116,122],"vector":[117,123],"(TC)":[119],"SVM,":[120,126],"one-class":[121],"(OC)":[125],"k-nearest":[127],"neighbor":[128],"classifier":[129,134],"(K-NN),":[130],"Naive":[132],"Bayes":[133],"(NB).":[135],"logged":[137],"experimental":[138,146],"data":[139],"captured":[141],"28":[143],"users.":[144],"show":[148,171],"that":[149,172],"offers":[153],"best":[155,179],"result":[157],"among":[158,180],"all":[159],"features,":[162],"followed":[163],"by":[164],"time.":[167],"Furthermore,":[168],"our":[169],"TC":[173],"SVM":[174],"KNN":[176],"perform":[177],"classifiers.":[183]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
