{"id":"https://openalex.org/W3034863102","doi":"https://doi.org/10.1109/tnsm.2021.3075503","title":"A New Method for Flow-Based Network Intrusion Detection Using the Inverse Potts Model","display_name":"A New Method for Flow-Based Network Intrusion Detection Using the Inverse Potts Model","publication_year":2021,"publication_date":"2021-04-26","ids":{"openalex":"https://openalex.org/W3034863102","doi":"https://doi.org/10.1109/tnsm.2021.3075503","mag":"3034863102"},"language":"en","primary_location":{"id":"doi:10.1109/tnsm.2021.3075503","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnsm.2021.3075503","pdf_url":null,"source":{"id":"https://openalex.org/S173527311","display_name":"IEEE Transactions on Network and Service Management","issn_l":"1932-4537","issn":["1932-4537","2373-7379"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Network and Service Management","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1910.07266","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Camila F. T. Pontes","orcid":"https://orcid.org/0000-0002-8726-3641"},"institutions":[{"id":"https://openalex.org/I150729083","display_name":"Universidade de Bras\u00edlia","ror":"https://ror.org/02xfp8v59","country_code":"BR","type":"education","lineage":["https://openalex.org/I150729083"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Camila F. T. Pontes","raw_affiliation_strings":["Computer Science Department, University of Brasilia, Brasilia, Brazil"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of Brasilia, Brasilia, Brazil","institution_ids":["https://openalex.org/I150729083"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Manuela M. C. de Souza","orcid":"https://orcid.org/0000-0002-8858-579X"},"institutions":[{"id":"https://openalex.org/I150729083","display_name":"Universidade de Bras\u00edlia","ror":"https://ror.org/02xfp8v59","country_code":"BR","type":"education","lineage":["https://openalex.org/I150729083"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Manuela M. C. de Souza","raw_affiliation_strings":["Computer Science Department, University of Brasilia, Brasilia, Brazil"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of Brasilia, Brasilia, Brazil","institution_ids":["https://openalex.org/I150729083"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Joao J. C. Gondim","orcid":"https://orcid.org/0000-0002-5873-7502"},"institutions":[{"id":"https://openalex.org/I150729083","display_name":"Universidade de Bras\u00edlia","ror":"https://ror.org/02xfp8v59","country_code":"BR","type":"education","lineage":["https://openalex.org/I150729083"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Joao J. C. Gondim","raw_affiliation_strings":["Computer Science Department, University of Brasilia, Brasilia, Brazil"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of Brasilia, Brasilia, Brazil","institution_ids":["https://openalex.org/I150729083"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Matt Bishop","orcid":null},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matt Bishop","raw_affiliation_strings":["Computer Science Department, University of California at Davis, Davis, CA, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of California at Davis, Davis, CA, USA","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"last","author":{"id":null,"display_name":"Marcelo Antonio Marotta","orcid":"https://orcid.org/0000-0003-1747-8441"},"institutions":[{"id":"https://openalex.org/I150729083","display_name":"Universidade de Bras\u00edlia","ror":"https://ror.org/02xfp8v59","country_code":"BR","type":"education","lineage":["https://openalex.org/I150729083"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Marcelo Antonio Marotta","raw_affiliation_strings":["Computer Science Department, University of Brasilia, Brasilia, Brazil"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of Brasilia, Brasilia, Brazil","institution_ids":["https://openalex.org/I150729083"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150729083"],"apc_list":null,"apc_paid":null,"fwci":11.8406,"has_fulltext":false,"cited_by_count":98,"citation_normalized_percentile":{"value":0.9889702,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"18","issue":"2","first_page":"1125","last_page":"1136"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.6507999897003174,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.6507999897003174,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.24699999392032623,"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/T12326","display_name":"Network Packet Processing and Optimization","score":0.03180000185966492,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/classifier","display_name":"Classifier (UML)","score":0.5782999992370605},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.5365999937057495},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.44780001044273376},{"id":"https://openalex.org/keywords/traffic-classification","display_name":"Traffic classification","score":0.43230000138282776},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.36660000681877136},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.35690000653266907},{"id":"https://openalex.org/keywords/traffic-analysis","display_name":"Traffic analysis","score":0.3555999994277954},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.35510000586509705},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.3497999906539917}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8356999754905701},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5782999992370605},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5426999926567078},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.5365999937057495},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.516700029373169},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4733000099658966},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.44780001044273376},{"id":"https://openalex.org/C169988225","wikidata":"https://www.wikidata.org/wiki/Q7832484","display_name":"Traffic classification","level":3,"score":0.43230000138282776},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.36660000681877136},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.35690000653266907},{"id":"https://openalex.org/C2781317605","wikidata":"https://www.wikidata.org/wiki/Q7832483","display_name":"Traffic analysis","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3425000011920929},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3400999903678894},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33090001344680786},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C2779190172","wikidata":"https://www.wikidata.org/wiki/Q4913888","display_name":"Binary data","level":3,"score":0.30820000171661377},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30000001192092896},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C114809511","wikidata":"https://www.wikidata.org/wiki/Q1412924","display_name":"Flow network","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C2780724565","wikidata":"https://www.wikidata.org/wiki/Q5227256","display_name":"Data classification","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C182590292","wikidata":"https://www.wikidata.org/wiki/Q989632","display_name":"Network security","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnsm.2021.3075503","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnsm.2021.3075503","pdf_url":null,"source":{"id":"https://openalex.org/S173527311","display_name":"IEEE Transactions on Network and Service Management","issn_l":"1932-4537","issn":["1932-4537","2373-7379"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Network and Service Management","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1910.07266","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1910.07266","pdf_url":"https://arxiv.org/pdf/1910.07266","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1910.07266","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1910.07266","pdf_url":"https://arxiv.org/pdf/1910.07266","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1369184054","display_name":null,"funder_award_id":"OAC-1739025","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1924689489","https://openalex.org/W1965511886","https://openalex.org/W1970361289","https://openalex.org/W1977556410","https://openalex.org/W1988195734","https://openalex.org/W1995341919","https://openalex.org/W2008545402","https://openalex.org/W2014662635","https://openalex.org/W2128420091","https://openalex.org/W2129302414","https://openalex.org/W2171331105","https://openalex.org/W2342408547","https://openalex.org/W2533784697","https://openalex.org/W2577513693","https://openalex.org/W2591712613","https://openalex.org/W2593641128","https://openalex.org/W2620580412","https://openalex.org/W2771399008","https://openalex.org/W2783047817","https://openalex.org/W2789828921","https://openalex.org/W2891833507","https://openalex.org/W2893143447","https://openalex.org/W2896215772","https://openalex.org/W2899653275","https://openalex.org/W2907467192","https://openalex.org/W2910197492","https://openalex.org/W2924689635","https://openalex.org/W2945976633","https://openalex.org/W2946445608","https://openalex.org/W2956315402","https://openalex.org/W2965561013","https://openalex.org/W2982682021","https://openalex.org/W2987177413","https://openalex.org/W2996766209","https://openalex.org/W3008695648","https://openalex.org/W3016038383","https://openalex.org/W3021973688","https://openalex.org/W3024476475","https://openalex.org/W4235787599","https://openalex.org/W4239510810","https://openalex.org/W4245055982","https://openalex.org/W4252028749","https://openalex.org/W4254177391","https://openalex.org/W6602539005","https://openalex.org/W6676769703","https://openalex.org/W6723978441","https://openalex.org/W6775245162"],"related_works":[],"abstract_inverted_index":{"Network":[0],"Intrusion":[1],"Detection":[2],"Systems":[3],"(NIDS)":[4],"play":[5],"an":[6,89],"important":[7],"role":[8],"as":[9,29],"tools":[10],"for":[11,32,66],"identifying":[12],"potential":[13],"network":[14],"threats.":[15],"In":[16,36],"the":[17,110,180],"context":[18],"of":[19,63,81,109,161],"ever-increasing":[20],"traffic":[21,34,204],"volume":[22],"on":[23,88,151,184],"computer":[24],"networks,":[25],"flow-based":[26,40,203],"NIDS":[27],"arise":[28],"good":[30],"solutions":[31],"real-time":[33],"classification.":[35,205],"recent":[37],"years,":[38],"different":[39,172,186],"classifiers":[41,53,77],"have":[42,54],"been":[43],"proposed":[44],"using":[45],"Machine":[46],"Learning":[47],"(ML)":[48],"algorithms.":[49],"Nevertheless,":[50],"classical":[51,176],"ML-based":[52,76,177],"some":[55],"limitations.":[56],"For":[57],"instance,":[58],"they":[59,93],"require":[60],"large":[61],"amounts":[62],"labeled":[64,152],"data":[65,91,104,173],"training,":[67],"which":[68,119],"might":[69],"be":[70,99,196],"difficult":[71],"to":[72,98,101,145,171,195,200],"obtain.":[73],"Additionally,":[74],"most":[75],"are":[78,94,116],"not":[79,95,121],"capable":[80,160],"domain":[82],"adaptation,":[83],"i.e.,":[84],"after":[85],"being":[86],"trained":[87],"specific":[90],"distribution,":[92],"general":[96],"enough":[97],"applied":[100],"other":[102],"related":[103],"distributions.":[105],"And,":[106],"finally,":[107],"many":[108],"models":[111],"inferred":[112],"by":[113],"these":[114,127],"algorithms":[115],"black":[117],"boxes,":[118],"do":[120],"provide":[122],"explainable":[123],"results.":[124],"To":[125],"overcome":[126],"limitations,":[128],"we":[129,192],"propose":[130],"a":[131,147,197],"new":[132],"algorithm,":[133],"called":[134],"Energy-based":[135],"Flow":[136],"Classifier":[137],"(EFC).":[138],"This":[139],"anomaly-based":[140],"classifier":[141],"uses":[142],"inverse":[143],"statistics":[144],"infer":[146],"statistical":[148],"model":[149],"based":[150],"benign":[153],"examples.":[154],"We":[155],"show":[156],"that":[157],"EFC":[158,194],"is":[159,168],"accurately":[162],"performing":[163],"binary":[164],"flow":[165],"classification":[166],"and":[167,190],"more":[169],"adaptable":[170],"distributions":[174],"than":[175],"classifiers.":[178],"Given":[179],"positive":[181],"results":[182],"obtained":[183],"three":[185],"datasets":[187],"(CIDDS-001,":[188],"CICIDS17":[189],"CICDDoS19),":[191],"consider":[193],"promising":[198],"algorithm":[199],"perform":[201],"robust":[202]},"counts_by_year":[{"year":2025,"cited_by_count":24},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":23},{"year":2022,"cited_by_count":27},{"year":2021,"cited_by_count":5}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2020-06-19T00:00:00"}
