{"id":"https://openalex.org/W4391769373","doi":"https://doi.org/10.1109/ccwc60891.2024.10427699","title":"Improving Network Traffic Anomaly Detection through Data Denoising and Unsupervised Learning","display_name":"Improving Network Traffic Anomaly Detection through Data Denoising and Unsupervised Learning","publication_year":2024,"publication_date":"2024-01-08","ids":{"openalex":"https://openalex.org/W4391769373","doi":"https://doi.org/10.1109/ccwc60891.2024.10427699"},"language":"en","primary_location":{"id":"doi:10.1109/ccwc60891.2024.10427699","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ccwc60891.2024.10427699","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC)","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/A5093917859","display_name":"James Talbott","orcid":null},"institutions":[{"id":"https://openalex.org/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James Talbott","raw_affiliation_strings":["Seattle University,Department of Computer Science,Seattle,USA","Department of Computer Science, Seattle University, Seattle, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Seattle University,Department of Computer Science,Seattle,USA","institution_ids":["https://openalex.org/I58610484"]},{"raw_affiliation_string":"Department of Computer Science, Seattle University, Seattle, USA","institution_ids":["https://openalex.org/I58610484"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093917860","display_name":"Joshua Palicka","orcid":null},"institutions":[{"id":"https://openalex.org/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joshua Palicka","raw_affiliation_strings":["Seattle University,Department of Computer Science,Seattle,USA","Department of Computer Science, Seattle University, Seattle, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Seattle University,Department of Computer Science,Seattle,USA","institution_ids":["https://openalex.org/I58610484"]},{"raw_affiliation_string":"Department of Computer Science, Seattle University, Seattle, USA","institution_ids":["https://openalex.org/I58610484"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030600121","display_name":"Alexander Sheardown","orcid":null},"institutions":[{"id":"https://openalex.org/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander Sheardown","raw_affiliation_strings":["Seattle University,Department of Computer Science,Seattle,USA","Department of Computer Science, Seattle University, Seattle, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Seattle University,Department of Computer Science,Seattle,USA","institution_ids":["https://openalex.org/I58610484"]},{"raw_affiliation_string":"Department of Computer Science, Seattle University, Seattle, USA","institution_ids":["https://openalex.org/I58610484"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053246589","display_name":"Wan D. Bae","orcid":"https://orcid.org/0000-0002-4611-5546"},"institutions":[{"id":"https://openalex.org/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wan D. Bae","raw_affiliation_strings":["Seattle University,Department of Computer Science,Seattle,USA","Department of Computer Science, Seattle University, Seattle, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Seattle University,Department of Computer Science,Seattle,USA","institution_ids":["https://openalex.org/I58610484"]},{"raw_affiliation_string":"Department of Computer Science, Seattle University, Seattle, USA","institution_ids":["https://openalex.org/I58610484"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053161595","display_name":"Matthew Horak","orcid":null},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew Horak","raw_affiliation_strings":["Amazon AWS Lambda,Seattle,USA","Amazon AWS Lambda, Seattle, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon AWS Lambda,Seattle,USA","institution_ids":["https://openalex.org/I1311688040"]},{"raw_affiliation_string":"Amazon AWS Lambda, Seattle, USA","institution_ids":["https://openalex.org/I1311688040"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01166568,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"0447","last_page":"0453"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9998000264167786,"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":0.9958000183105469,"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/autoencoder","display_name":"Autoencoder","score":0.911026656627655},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7943508625030518},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.734512448310852},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7046569585800171},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6708586812019348},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6137909889221191},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6099725961685181},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.5995948314666748},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5486893057823181},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5219730138778687},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5169038772583008},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49146783351898193},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4666825234889984},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.46559011936187744},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.46188846230506897},{"id":"https://openalex.org/keywords/pipeline-transport","display_name":"Pipeline transport","score":0.45672401785850525},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.44564688205718994},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39399099349975586},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11230123043060303},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.07158926129341125}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.911026656627655},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7943508625030518},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.734512448310852},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7046569585800171},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6708586812019348},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6137909889221191},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6099725961685181},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.5995948314666748},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5486893057823181},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5219730138778687},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5169038772583008},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49146783351898193},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4666825234889984},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.46559011936187744},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.46188846230506897},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.45672401785850525},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.44564688205718994},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39399099349975586},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11230123043060303},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.07158926129341125},{"id":"https://openalex.org/C87717796","wikidata":"https://www.wikidata.org/wiki/Q146326","display_name":"Environmental engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.0},{"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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccwc60891.2024.10427699","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ccwc60891.2024.10427699","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1598260899","https://openalex.org/W1995443851","https://openalex.org/W2076758681","https://openalex.org/W2104837959","https://openalex.org/W2296719434","https://openalex.org/W2884424765","https://openalex.org/W2899638272","https://openalex.org/W2912097776","https://openalex.org/W3006123093","https://openalex.org/W3007481080","https://openalex.org/W3040266635","https://openalex.org/W3102138934","https://openalex.org/W3104917574","https://openalex.org/W3135550350","https://openalex.org/W3149467830","https://openalex.org/W3152794541","https://openalex.org/W3171512724","https://openalex.org/W3206382155","https://openalex.org/W3213140656","https://openalex.org/W4210954769","https://openalex.org/W4297786314","https://openalex.org/W4313591247","https://openalex.org/W4319999485","https://openalex.org/W6748102297"],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W3194885736","https://openalex.org/W4363671829","https://openalex.org/W4285233543","https://openalex.org/W2806873178","https://openalex.org/W2965146396","https://openalex.org/W2770818364","https://openalex.org/W4404095322","https://openalex.org/W4312416532","https://openalex.org/W4230838436"],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,38,105,113,140],"technology":[3],"have":[4,26,33],"allowed":[5],"people":[6],"to":[7,18,72],"be":[8],"more":[9,16,28,88,94,170],"connected,":[10],"but":[11,87],"it":[12],"also":[13],"makes":[14],"them":[15],"vulnerable":[17],"cyber":[19],"threats.":[20],"Hence,":[21],"advanced":[22],"threat":[23],"detection":[24,48],"systems":[25],"become":[27],"important":[29],"than":[30,97,172,187],"ever.":[31],"We":[32,101],"built":[34],"upon":[35],"recent":[36],"work":[37],"unsupervised":[39],"deep":[40],"learning":[41],"and":[42,69,78,93,109,117,181],"further":[43,131],"investigated":[44],"their":[45],"two-phase":[46,58,154],"anomaly":[47],"process":[49,144],"for":[50],"network":[51],"traffic":[52],"data.":[53],"This":[54],"paper":[55],"extends":[56],"that":[57,66,84,152],"pipeline":[59,168,184],"framework,":[60],"which":[61],"uses":[62,80],"a":[63,142,146],"first":[64,107],"model":[65,83,99,191],"is":[67,85],"faster":[68,92,186],"prioritizes":[70],"sensitivity":[71],"reduce":[73],"the":[74,81,106,114,120,127,133,136,153,162,167,183],"incoming":[75],"data":[76],"load":[77],"then":[79],"second":[82,115],"slower":[86],"accurate,":[89],"thereby":[90],"achieving":[91],"accurate":[95],"predictions":[96],"either":[98],"alone.":[100],"implement":[102],"autoencoder":[103,174],"variants":[104,112],"phase":[108],"isolation":[110,189],"forest":[111,190],"phase,":[116],"we":[118,138],"present":[119],"performance":[121,134],"analysis":[122],"of":[123,126,135,178,195],"various":[124],"examples":[125],"two":[128],"methods.":[129],"To":[130],"improve":[132],"pipeline,":[137,156],"introduce":[139],"preprocessing":[141],"denoising":[143],"using":[145],"separate":[147],"autoencoder.":[148],"Our":[149],"results":[150],"confirm":[151],"prediction":[155,179],"with":[157],"carefully":[158],"fine-tuned":[159],"hyper-parameters,":[160],"outperforms":[161],"individual":[163],"standalone":[164],"models:":[165],"(1)":[166],"predicts":[169,185],"accurately":[171],"an":[173,188],"without":[175,192],"significant":[176,193],"loss":[177,194],"speed,":[180],"(2)":[182],"accuracy.":[196]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}