{"id":"https://openalex.org/W4411302502","doi":"https://doi.org/10.1186/s42400-025-00364-7","title":"A network intrusion detection method based on contrastive learning and Bayesian Gaussian Mixture Model","display_name":"A network intrusion detection method based on contrastive learning and Bayesian Gaussian Mixture Model","publication_year":2025,"publication_date":"2025-06-15","ids":{"openalex":"https://openalex.org/W4411302502","doi":"https://doi.org/10.1186/s42400-025-00364-7"},"language":"en","primary_location":{"id":"doi:10.1186/s42400-025-00364-7","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s42400-025-00364-7","pdf_url":"https://cybersecurity.springeropen.com/counter/pdf/10.1186/s42400-025-00364-7","source":{"id":"https://openalex.org/S3035238565","display_name":"Cybersecurity","issn_l":"2523-3246","issn":["2523-3246"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybersecurity","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://cybersecurity.springeropen.com/counter/pdf/10.1186/s42400-025-00364-7","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079954560","display_name":"Lei Liu","orcid":"https://orcid.org/0000-0002-4852-7739"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liyou Liu","raw_affiliation_strings":["School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China"],"affiliations":[{"raw_affiliation_string":"School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041210918","display_name":"Ming Xu","orcid":"https://orcid.org/0000-0001-9332-5258"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Xu","raw_affiliation_strings":["School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China"],"affiliations":[{"raw_affiliation_string":"School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China","institution_ids":["https://openalex.org/I50760025"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5079954560"],"corresponding_institution_ids":["https://openalex.org/I50760025"],"apc_list":null,"apc_paid":null,"fwci":3.1149,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.92101575,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"8","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":1.0,"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":1.0,"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":0.9944000244140625,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9929999709129333,"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":0.6152696013450623},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.5460084676742554},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5344893932342529},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.5285921096801758},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5023691654205322},{"id":"https://openalex.org/keywords/gaussian-network-model","display_name":"Gaussian network model","score":0.4809662401676178},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44356653094291687},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41582658886909485},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.39673471450805664},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3811761736869812},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.06453636288642883}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6152696013450623},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.5460084676742554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5344893932342529},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.5285921096801758},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5023691654205322},{"id":"https://openalex.org/C166550679","wikidata":"https://www.wikidata.org/wiki/Q263400","display_name":"Gaussian network model","level":3,"score":0.4809662401676178},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44356653094291687},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41582658886909485},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.39673471450805664},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3811761736869812},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.06453636288642883},{"id":"https://openalex.org/C147597530","wikidata":"https://www.wikidata.org/wiki/Q369472","display_name":"Computational chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s42400-025-00364-7","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s42400-025-00364-7","pdf_url":"https://cybersecurity.springeropen.com/counter/pdf/10.1186/s42400-025-00364-7","source":{"id":"https://openalex.org/S3035238565","display_name":"Cybersecurity","issn_l":"2523-3246","issn":["2523-3246"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybersecurity","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:6848b916faa24b0c8a19045dbfcf85e6","is_oa":true,"landing_page_url":"https://doaj.org/article/6848b916faa24b0c8a19045dbfcf85e6","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Cybersecurity, Vol 8, Iss 1, Pp 1-17 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s42400-025-00364-7","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s42400-025-00364-7","pdf_url":"https://cybersecurity.springeropen.com/counter/pdf/10.1186/s42400-025-00364-7","source":{"id":"https://openalex.org/S3035238565","display_name":"Cybersecurity","issn_l":"2523-3246","issn":["2523-3246"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybersecurity","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4411302502.pdf","grobid_xml":"https://content.openalex.org/works/W4411302502.grobid-xml"},"referenced_works_count":49,"referenced_works":["https://openalex.org/W2015887370","https://openalex.org/W2049633694","https://openalex.org/W2225156818","https://openalex.org/W2296509296","https://openalex.org/W2982853004","https://openalex.org/W3005630930","https://openalex.org/W3012981875","https://openalex.org/W3014732532","https://openalex.org/W3018495625","https://openalex.org/W3024905798","https://openalex.org/W3106741970","https://openalex.org/W3114632476","https://openalex.org/W3121453273","https://openalex.org/W3132199788","https://openalex.org/W3156636935","https://openalex.org/W3157331533","https://openalex.org/W3158938497","https://openalex.org/W3194111370","https://openalex.org/W3198200730","https://openalex.org/W3199399730","https://openalex.org/W4206130810","https://openalex.org/W4212863985","https://openalex.org/W4214699222","https://openalex.org/W4220982217","https://openalex.org/W4221161674","https://openalex.org/W4226076258","https://openalex.org/W4280610250","https://openalex.org/W4281759695","https://openalex.org/W4283210230","https://openalex.org/W4285761032","https://openalex.org/W4292506131","https://openalex.org/W4295788744","https://openalex.org/W4309346044","https://openalex.org/W4312823356","https://openalex.org/W4313654696","https://openalex.org/W4317423310","https://openalex.org/W4318962769","https://openalex.org/W4319264752","https://openalex.org/W4320169013","https://openalex.org/W4321240473","https://openalex.org/W4366606623","https://openalex.org/W4382468418","https://openalex.org/W4388573952","https://openalex.org/W4391591712","https://openalex.org/W4391812155","https://openalex.org/W4392120324","https://openalex.org/W4392909503","https://openalex.org/W4393190094","https://openalex.org/W4394586044"],"related_works":["https://openalex.org/W1578916557","https://openalex.org/W4295035285","https://openalex.org/W2350507978","https://openalex.org/W2365475731","https://openalex.org/W2016260880","https://openalex.org/W2073372811","https://openalex.org/W2386291451","https://openalex.org/W2008969177","https://openalex.org/W1998316682","https://openalex.org/W2122170741"],"abstract_inverted_index":{"Abstract":[0],"Network":[1],"Intrusion":[2],"Detection":[3],"Systems":[4],"(NIDS)":[5],"are":[6],"essential":[7],"for":[8,125,139],"safeguarding":[9],"networks":[10],"against":[11],"malicious":[12,109,153],"activities.":[13],"However,":[14],"existing":[15],"machine":[16],"learning-based":[17],"NIDS":[18],"often":[19],"require":[20],"complex":[21,37],"feature":[22,116,127,137],"engineering,":[23],"which":[24,193,217],"demands":[25],"significant":[26],"domain":[27],"expertise":[28],"and":[29,58,77,103,108,114,152,190,196,214,220],"experimentation,":[30],"leading":[31],"to":[32,95,149],"suboptimal":[33],"model":[34,94],"performance":[35,146],"in":[36,52,61],"network":[38,177],"environments.":[39],"In":[40],"contrast,":[41],"deep":[42],"learning":[43,76,89],"approaches,":[44],"while":[45],"powerful,":[46],"struggle":[47],"with":[48],"imbalanced":[49],"data,":[50],"resulting":[51],"a":[53,71,86],"bias":[54],"towards":[55],"normal":[56,101,107,151],"traffic":[57,102],"reduced":[59],"effectiveness":[60,162],"detecting":[62],"rare":[63,140],"attacks.":[64,141],"To":[65],"address":[66],"these":[67],"issues,":[68],"we":[69,84],"propose":[70,85],"method":[72,166,186,210],"that":[73,91],"combines":[74],"contrastive":[75,88],"Bayesian":[78],"Gaussian":[79],"Mixture":[80],"Model":[81],"(BGMM).":[82],"Specifically,":[83],"novel":[87],"loss":[90],"enables":[92],"the":[93,98,104,123,133,156,164,181,184,200,205,208,224],"automatically":[96],"learn":[97],"similarity":[99],"within":[100],"distinction":[105],"between":[106],"traffic,":[110],"thereby":[111],"generating":[112],"robust":[113],"distinguishable":[115],"representations.":[117],"This":[118],"approach":[119],"not":[120],"only":[121],"eliminates":[122],"need":[124],"manual":[126],"engineering":[128],"but":[129],"also":[130],"helps":[131],"alleviate":[132],"issue":[134],"of":[135,158,163],"weak":[136],"representations":[138],"BGMM":[142],"further":[143],"enhances":[144],"detection":[145],"by":[147],"adapting":[148],"both":[150],"patterns":[154],"through":[155,169],"use":[157],"multiple":[159],"components.":[160],"The":[161],"proposed":[165,185,209],"is":[167,194,218],"validated":[168],"extensive":[170],"experiments":[171],"on":[172],"two":[173],"widely":[174],"used":[175],"modern":[176],"intrusion":[178],"datasets.":[179],"On":[180,204],"UNSW-NB15":[182],"dataset,":[183,207],"achieves":[187,211],"91.27%":[188],"accuracy":[189,213],"92.30%":[191],"F1-score,":[192,216],"1.85%":[195],"2.35%":[197],"better":[198,222],"than":[199,223],"state-of-the-art":[201],"(SOTA)":[202],"method.":[203,226],"Distrinet-CIC-IDS2017":[206],"99.66%":[212],"99.12%":[215],"0.05%":[219],"0.12%":[221],"SOTA":[225]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
