{"id":"https://openalex.org/W4388015968","doi":"https://doi.org/10.1109/aibthings58340.2023.10292485","title":"Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection","display_name":"Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection","publication_year":2023,"publication_date":"2023-09-16","ids":{"openalex":"https://openalex.org/W4388015968","doi":"https://doi.org/10.1109/aibthings58340.2023.10292485"},"language":"en","primary_location":{"id":"doi:10.1109/aibthings58340.2023.10292485","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aibthings58340.2023.10292485","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings)","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/A5007103197","display_name":"Tosin Ige","orcid":null},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tosin Ige","raw_affiliation_strings":["University of Texas at El Paso,Department of Computer Science,Texas,USA","Department of Computer Science, University of Texas at El Paso, Texas, USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at El Paso,Department of Computer Science,Texas,USA","institution_ids":["https://openalex.org/I164936912"]},{"raw_affiliation_string":"Department of Computer Science, University of Texas at El Paso, Texas, USA","institution_ids":["https://openalex.org/I164936912"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020091080","display_name":"Christopher Kiekintveld","orcid":"https://orcid.org/0000-0003-0615-9584"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christopher Kiekintveld","raw_affiliation_strings":["University of Texas at El Paso,Department of Computer Science,Texas,USA","Department of Computer Science, University of Texas at El Paso, Texas, USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at El Paso,Department of Computer Science,Texas,USA","institution_ids":["https://openalex.org/I164936912"]},{"raw_affiliation_string":"Department of Computer Science, University of Texas at El Paso, Texas, USA","institution_ids":["https://openalex.org/I164936912"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5007103197"],"corresponding_institution_ids":["https://openalex.org/I164936912"],"apc_list":null,"apc_paid":null,"fwci":3.21,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.92529934,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9997000098228455,"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":0.9997000098228455,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9977999925613403,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9955999851226807,"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/multinomial-distribution","display_name":"Multinomial distribution","score":0.7312973737716675},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6548320055007935},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6277357339859009},{"id":"https://openalex.org/keywords/bernoullis-principle","display_name":"Bernoulli's principle","score":0.5966970920562744},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5900614857673645},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5818271040916443},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5614909529685974},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5373839735984802},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.4964492917060852},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.47278547286987305},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.4520995616912842},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.4480922818183899},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.41748809814453125},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3823193609714508},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23881450295448303},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22474589943885803},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.08477172255516052},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07505655288696289}],"concepts":[{"id":"https://openalex.org/C192065140","wikidata":"https://www.wikidata.org/wiki/Q1147928","display_name":"Multinomial distribution","level":2,"score":0.7312973737716675},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6548320055007935},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6277357339859009},{"id":"https://openalex.org/C152361515","wikidata":"https://www.wikidata.org/wiki/Q181328","display_name":"Bernoulli's principle","level":2,"score":0.5966970920562744},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5900614857673645},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5818271040916443},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5614909529685974},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5373839735984802},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.4964492917060852},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.47278547286987305},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4520995616912842},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.4480922818183899},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.41748809814453125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3823193609714508},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23881450295448303},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22474589943885803},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.08477172255516052},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07505655288696289},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aibthings58340.2023.10292485","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aibthings58340.2023.10292485","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2084496302","https://openalex.org/W2794383999","https://openalex.org/W2964992537","https://openalex.org/W2977530153","https://openalex.org/W3117252255","https://openalex.org/W4226469277","https://openalex.org/W4283700879","https://openalex.org/W4285158626","https://openalex.org/W4285193763","https://openalex.org/W4285227650","https://openalex.org/W4309224799","https://openalex.org/W4321488544","https://openalex.org/W4362612759","https://openalex.org/W7064961771"],"related_works":["https://openalex.org/W2955220190","https://openalex.org/W2305090462","https://openalex.org/W2052791731","https://openalex.org/W2055203911","https://openalex.org/W2117588607","https://openalex.org/W1976212024","https://openalex.org/W2073599813","https://openalex.org/W2058461761","https://openalex.org/W2537862391","https://openalex.org/W2417174640"],"abstract_inverted_index":{"Bayesian":[0,40,74],"classifiers":[1],"perform":[2],"well":[3],"when":[4],"each":[5,37,61,71,134,145],"of":[6,12,26,36,39,73,81,105,113,122],"the":[7,13,34,86,89],"features":[8,160],"is":[9,16,29,57,88,141],"completely":[10],"independent":[11],"other":[14],"which":[15,164],"not":[17],"always":[18],"valid":[19],"in":[20,49],"real":[21],"world":[22],"application.":[23],"The":[24],"aim":[25],"this":[27],"study":[28],"to":[30,53,144,156],"implement":[31],"and":[32,44,52,64,84,131,139,179],"compare":[33],"performances":[35,138],"variant":[38,72],"classifier":[41,146,149,169],"(Multinomial,":[42],"Bernoulli,":[43],"Gaussian)":[45],"on":[46,152],"anomaly":[47,153],"detection":[48,154],"network":[50],"intrusion,":[51],"investigate":[54],"whether":[55],"there":[56],"any":[58],"association":[59],"between":[60],"variant\u2019s":[62],"assumption":[63,79,87,158],"their":[65,96],"performance.":[66],"Our":[67],"investigation":[68],"showed":[69],"that":[70,85,94,101,133,159],"algorithm":[75],"blindly":[76],"follows":[77],"its":[78,157],"regardless":[80],"feature":[82],"property,":[83],"single":[90],"most":[91],"important":[92],"factor":[93],"influences":[95],"accuracy.":[97],"Experimental":[98],"results":[99],"show":[100],"Bernoulli":[102],"has":[103,111,120],"accuracy":[104,112,121,140],"69.9%":[106],"test":[107,115,124],"(71%":[108],"train),":[109,117],"Multinomial":[110],"31.2%":[114],"(31.2%":[116],"while":[118,167],"Gaussian":[119,148],"81.69%":[123],"(82.84%":[125],"train).":[126],"Going":[127],"deeper,":[128],"we":[129],"investigated":[130],"found":[132],"Na\u00efve":[135],"Bayes":[136],"variants":[137],"largely":[142],"due":[143,155],"assumption,":[147],"performed":[150],"best":[151],"follow":[161],"normal":[162],"distributions":[163],"are":[165],"continuous,":[166],"multinomial":[168,180],"have":[170],"a":[171],"dismal":[172],"performance":[173],"as":[174],"it":[175],"simply":[176],"assumes":[177],"discreet":[178],"distribution.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}