{"id":"https://openalex.org/W4395029958","doi":"https://doi.org/10.1109/percomworkshops59983.2024.10502498","title":"IoT Network Traffic Analysis with Deep Learning","display_name":"IoT Network Traffic Analysis with Deep Learning","publication_year":2024,"publication_date":"2024-03-11","ids":{"openalex":"https://openalex.org/W4395029958","doi":"https://doi.org/10.1109/percomworkshops59983.2024.10502498"},"language":"en","primary_location":{"id":"doi:10.1109/percomworkshops59983.2024.10502498","is_oa":false,"landing_page_url":"https://doi.org/10.1109/percomworkshops59983.2024.10502498","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100313251","display_name":"Mei Liu","orcid":"https://orcid.org/0000-0001-8513-1402"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Mei Liu","raw_affiliation_strings":["Deakin University,School of IT,Australia","School of IT, Deakin University, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Deakin University,School of IT,Australia","institution_ids":["https://openalex.org/I149704539"]},{"raw_affiliation_string":"School of IT, Deakin University, Australia","institution_ids":["https://openalex.org/I149704539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041666592","display_name":"Leon Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Leon Yang","raw_affiliation_strings":["Deakin University,School of IT,Australia","School of IT, Deakin University, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Deakin University,School of IT,Australia","institution_ids":["https://openalex.org/I149704539"]},{"raw_affiliation_string":"School of IT, Deakin University, Australia","institution_ids":["https://openalex.org/I149704539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149704539"],"apc_list":null,"apc_paid":null,"fwci":4.468,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.95518499,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"184","last_page":"189"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9470000267028809,"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":0.9470000267028809,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7420905828475952},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5899708271026611},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4499571621417999},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.3861895501613617}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7420905828475952},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5899708271026611},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4499571621417999},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3861895501613617}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/percomworkshops59983.2024.10502498","is_oa":false,"landing_page_url":"https://doi.org/10.1109/percomworkshops59983.2024.10502498","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)","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":23,"referenced_works":["https://openalex.org/W2122646361","https://openalex.org/W2241127870","https://openalex.org/W2735916625","https://openalex.org/W2743138268","https://openalex.org/W2768800090","https://openalex.org/W2910068345","https://openalex.org/W2920946673","https://openalex.org/W2963684088","https://openalex.org/W3008578055","https://openalex.org/W3027374119","https://openalex.org/W3030840133","https://openalex.org/W3040266635","https://openalex.org/W3048332928","https://openalex.org/W3089955744","https://openalex.org/W3118153437","https://openalex.org/W3135550350","https://openalex.org/W3164999323","https://openalex.org/W3193416433","https://openalex.org/W3196584685","https://openalex.org/W4205388843","https://openalex.org/W4307290973","https://openalex.org/W6685352114","https://openalex.org/W6758101687"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W4323565446"],"abstract_inverted_index":{"As":[0],"IoT":[1,101],"networks":[2,102],"become":[3],"more":[4],"complex":[5],"and":[6,18,25,34,41,66,83,94,121],"generate":[7],"massive":[8],"amounts":[9,38],"of":[10,39,141,150],"dynamic":[11],"data,":[12],"it":[13,61,96],"is":[14],"difficult":[15],"to":[16,56,63,98],"monitor":[17,99],"detect":[19,57,64],"anomalies":[20,68],"using":[21,46,117,125],"traditional":[22],"statistical":[23],"methods":[24],"machine":[26],"learning":[27,30,48,78,119],"methods.":[28],"Deep":[29],"algorithms":[31,79],"can":[32,42,80,88],"process":[33],"learn":[35],"from":[36],"large":[37,100],"data":[40,55],"also":[43],"be":[44,81],"trained":[45],"unsupervised":[47],"techniques,":[49],"meaning":[50],"they":[51,87],"don\u2019t":[52],"require":[53],"labelled":[54],"anomalies.":[58],"This":[59],"makes":[60],"possible":[62],"new":[65],"unknown":[67],"that":[69],"may":[70],"not":[71],"have":[72],"been":[73],"detected":[74],"before.":[75],"Also,":[76],"deep":[77,118,143],"automated":[82],"highly":[84],"scalable;":[85],"thereby,":[86],"run":[89],"continuously":[90],"in":[91],"the":[92,113,129,138],"backend":[93],"make":[95],"achievable":[97],"instantly.":[103],"In":[104],"this":[105],"work,":[106],"we":[107],"conduct":[108],"a":[109,123],"literature":[110],"review":[111],"on":[112,128],"most":[114],"recent":[115],"works":[116],"techniques":[120,127],"implement":[122],"model":[124],"ensemble":[126],"KDD":[130],"Cup":[131],"99":[132],"dataset.":[133],"The":[134],"experimental":[135],"results":[136],"showcase":[137],"impressive":[139],"performance":[140],"our":[142],"anomaly":[144],"detection":[145],"model,":[146],"achieving":[147],"an":[148],"accuracy":[149],"over":[151],"98%.":[152]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
