{"id":"https://openalex.org/W4226133063","doi":"https://doi.org/10.1145/3501774.3501789","title":"Improving Multiclass Classification of Cybersecurity Breaches in Railway Infrastructure using Imbalanced Learning","display_name":"Improving Multiclass Classification of Cybersecurity Breaches in Railway Infrastructure using Imbalanced Learning","publication_year":2021,"publication_date":"2021-11-19","ids":{"openalex":"https://openalex.org/W4226133063","doi":"https://doi.org/10.1145/3501774.3501789"},"language":"en","primary_location":{"id":"doi:10.1145/3501774.3501789","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3501774.3501789","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 2nd European Symposium on Software Engineering","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/A5034070929","display_name":"Aleksandr N. Nebaba","orcid":null},"institutions":[{"id":"https://openalex.org/I4210086666","display_name":"Rostov State Transport University","ror":"https://ror.org/002xnzp34","country_code":"RU","type":"education","lineage":["https://openalex.org/I4210086666"]}],"countries":["RU"],"is_corresponding":true,"raw_author_name":"Aleksandr N. Nebaba","raw_affiliation_strings":["Informatics, Rostov State Transport University, Russia"],"affiliations":[{"raw_affiliation_string":"Informatics, Rostov State Transport University, Russia","institution_ids":["https://openalex.org/I4210086666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067825806","display_name":"Ilias \u039a. Savvas","orcid":"https://orcid.org/0000-0003-2060-0911"},"institutions":[{"id":"https://openalex.org/I145722265","display_name":"University of Thessaly","ror":"https://ror.org/04v4g9h31","country_code":"GR","type":"education","lineage":["https://openalex.org/I145722265"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Ilias K. Savvas","raw_affiliation_strings":["Dept. of Digital Systems, University of Thessaly, Greece"],"affiliations":[{"raw_affiliation_string":"Dept. of Digital Systems, University of Thessaly, Greece","institution_ids":["https://openalex.org/I145722265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026627052","display_name":"Maria A. Butakova","orcid":"https://orcid.org/0000-0002-6167-7942"},"institutions":[{"id":"https://openalex.org/I137534880","display_name":"Southern Federal University","ror":"https://ror.org/01tv9ph92","country_code":"RU","type":"education","lineage":["https://openalex.org/I137534880"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Maria A. Butakova","raw_affiliation_strings":["The Smart Materials Research Institute, Southern Federal University, Russia"],"affiliations":[{"raw_affiliation_string":"The Smart Materials Research Institute, Southern Federal University, Russia","institution_ids":["https://openalex.org/I137534880"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015307669","display_name":"\u0410. \u0412. \u0427\u0435\u0440\u043d\u043e\u0432","orcid":"https://orcid.org/0000-0002-2922-330X"},"institutions":[{"id":"https://openalex.org/I137534880","display_name":"Southern Federal University","ror":"https://ror.org/01tv9ph92","country_code":"RU","type":"education","lineage":["https://openalex.org/I137534880"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Andrey V. Chernov","raw_affiliation_strings":["The Smart Materials Research Institute, Southern Federal University, Russia"],"affiliations":[{"raw_affiliation_string":"The Smart Materials Research Institute, Southern Federal University, Russia","institution_ids":["https://openalex.org/I137534880"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088252049","display_name":"Petr S. Shevchuk","orcid":null},"institutions":[{"id":"https://openalex.org/I3020615197","display_name":"Institute of Service and Entrepreneurship of DGTU","ror":"https://ror.org/01rzj1953","country_code":"RU","type":"education","lineage":["https://openalex.org/I3020615197"]},{"id":"https://openalex.org/I4210097717","display_name":"Don State Technical University","ror":"https://ror.org/00x5je630","country_code":"RU","type":"education","lineage":["https://openalex.org/I4210097717"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Petr S. Shevchuk","raw_affiliation_strings":["Don State Technical University, Russia"],"affiliations":[{"raw_affiliation_string":"Don State Technical University, Russia","institution_ids":["https://openalex.org/I3020615197","https://openalex.org/I4210097717"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5034070929"],"corresponding_institution_ids":["https://openalex.org/I4210086666"],"apc_list":null,"apc_paid":null,"fwci":0.14,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.5931738,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"100","last_page":"105"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T13429","display_name":"Electricity Theft Detection Techniques","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9803000092506409,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/computer-science","display_name":"Computer science","score":0.7690380811691284},{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.7127453684806824},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.708949863910675},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6896690130233765},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6181906461715698},{"id":"https://openalex.org/keywords/multiclass-classification","display_name":"Multiclass classification","score":0.6106745600700378},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5175158977508545},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.49205249547958374},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4844267666339874},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.41854652762413025},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08560895919799805}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7690380811691284},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.7127453684806824},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.708949863910675},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6896690130233765},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6181906461715698},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.6106745600700378},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5175158977508545},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.49205249547958374},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4844267666339874},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.41854652762413025},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08560895919799805},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3501774.3501789","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3501774.3501789","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 2nd European Symposium on Software Engineering","raw_type":"proceedings-article"},{"id":"pmh:oai:ir.lib.uth.gr:11615/77130","is_oa":false,"landing_page_url":"http://hdl.handle.net/11615/77130","pdf_url":null,"source":{"id":"https://openalex.org/S4306400243","display_name":"University of Thessaly Institutional Repository (University of Thessaly)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I145722265","host_organization_name":"University of Thessaly","host_organization_lineage":["https://openalex.org/I145722265"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ACM International Conference Proceeding Series","raw_type":"conferenceItem"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6299999952316284,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G8093749855","display_name":null,"funder_award_id":"19-01-00246-a, 19-07-00329-a","funder_id":"https://openalex.org/F4320321079","funder_display_name":"Russian Foundation for Basic Research"}],"funders":[{"id":"https://openalex.org/F4320321079","display_name":"Russian Foundation for Basic Research","ror":"https://ror.org/02mh1ke95"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1010554929","https://openalex.org/W2118978333","https://openalex.org/W2127766642","https://openalex.org/W2148143831","https://openalex.org/W2612802743","https://openalex.org/W2756504887","https://openalex.org/W2759183755","https://openalex.org/W2980541546","https://openalex.org/W2981067610","https://openalex.org/W3120063549","https://openalex.org/W4243367342","https://openalex.org/W4243952553","https://openalex.org/W4256342583"],"related_works":["https://openalex.org/W4401045170","https://openalex.org/W3172259201","https://openalex.org/W2903618681","https://openalex.org/W3184937791","https://openalex.org/W4367336074","https://openalex.org/W4401052546","https://openalex.org/W3154045278","https://openalex.org/W4379620016","https://openalex.org/W4393666307","https://openalex.org/W3210764983"],"abstract_inverted_index":{"Machine":[0],"learning":[1,127,166,187,205,233],"approaches":[2],"and":[3,12,22,29,40,62,81,114,142,175,193,199],"algorithms":[4],"are":[5,16,26],"spreading":[6],"in":[7,10,49,120,154,222],"wide":[8],"areas":[9],"research":[11],"technology.":[13],"Cybersecurity":[14],"breaches":[15,38,77,99,153],"the":[17,33,72,75,85,109,116,122,137,140,147,160,176,214,217,223,231],"common":[18],"anomalies":[19],"for":[20,125,219],"networked":[21],"distributed":[23],"infrastructures":[24],"which":[25],"monitored,":[27],"registered,":[28],"described":[30],"carefully.":[31],"However,":[32],"description":[34],"of":[35,65,74,87,105,111,139,162,216],"each":[36],"security":[37,67,76,98],"episode":[39],"its":[41],"classification":[42,90,101,141,221],"is":[43,91],"still":[44],"a":[45,59,79],"difficult":[46],"problem,":[47],"especially":[48],"highly":[50],"complex":[51],"telecommunication":[52,156],"infrastructure.":[53,157],"Railway":[54],"information":[55],"infrastructure":[56],"usually":[57],"has":[58,78],"large":[60,63],"scale":[61],"diversity":[64],"possible":[66,117],"breaches.":[68],"Today's":[69],"situation":[70],"shows":[71],"registering":[73],"mature":[80],"stable":[82],"character,":[83],"but":[84],"problem":[86,113],"their":[88],"automated":[89],"not":[92],"solved":[93],"completely.":[94],"Many":[95],"studies":[96],"on":[97,146,197,230],"multiclass":[100,128,220],"show":[102],"inadequate":[103],"accuracy":[104,138,218],"classification.":[106,129],"We":[107,158,182],"investigated":[108],"origins":[110],"this":[112],"suggested":[115],"roots":[118],"consist":[119],"disbalance":[121],"datasets":[123,150],"used":[124],"machine":[126,186],"Thus,":[130],"we":[131],"proposed":[132,211],"an":[133],"approach":[134,145,212],"to":[135,202,227],"improve":[136],"verified":[143],"our":[144],"really":[148],"collected":[149],"with":[151,179,207],"cybersecurity":[152],"railway":[155],"analyzed":[159],"results":[161],"applying":[163],"three":[164,185],"imbalanced":[165,232],"methodologies,":[167],"namely":[168,189],"random":[169],"oversampling,":[170],"synthetic":[171],"minority":[172],"oversampling":[173],"technique,":[174],"last":[177],"one":[178],"Tomek":[180],"links.":[181],"have":[183],"implemented":[184],"algorithms,":[188],"Na\u00efve":[190],"Bayes,":[191],"K-means,":[192],"support":[194],"vector":[195],"machine,":[196],"disbalances":[198],"balanced":[200],"data":[201],"estimate":[203],"imbalance":[204],"methodologies":[206],"comparing":[208],"results.":[209],"The":[210],"demonstrated":[213],"increase":[215],"range":[224],"from":[225],"30":[226],"41%,":[228],"depending":[229],"technique.":[234]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2022-05-05T00:00:00"}