{"id":"https://openalex.org/W4401017200","doi":"https://doi.org/10.1109/cvidl62147.2024.10603582","title":"IDS-GAN: Adversarial Attack against Intrusion Detection Based on Generative Adversarial Networks","display_name":"IDS-GAN: Adversarial Attack against Intrusion Detection Based on Generative Adversarial Networks","publication_year":2024,"publication_date":"2024-04-19","ids":{"openalex":"https://openalex.org/W4401017200","doi":"https://doi.org/10.1109/cvidl62147.2024.10603582"},"language":"en","primary_location":{"id":"doi:10.1109/cvidl62147.2024.10603582","doi":"https://doi.org/10.1109/cvidl62147.2024.10603582","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvidl62147.2024.10603582","pdf_url":null,"source":{"id":"https://openalex.org/S7407086714","display_name":"International Conference on Computer Vision, Image and Deep Learning (CVIDL)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5103234968","display_name":"Di Wang","orcid":"https://orcid.org/0009-0002-7610-2786","observed_orcids":["https://orcid.org/0009-0002-7610-2786"]},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di Wang","raw_affiliation_strings":["PLA Information Engineering University,School of Cyberspace Security,Zhengzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"PLA Information Engineering University,School of Cyberspace Security,Zhengzhou,China","institution_ids":["https://openalex.org/I169689159"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023645826","display_name":"Xuemeng Wang","orcid":"https://orcid.org/0000-0001-8226-0196","observed_orcids":["https://orcid.org/0000-0001-8226-0196"]},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuemeng Wang","raw_affiliation_strings":["PLA Information Engineering University,School of Cyberspace Security,Zhengzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"PLA Information Engineering University,School of Cyberspace Security,Zhengzhou,China","institution_ids":["https://openalex.org/I169689159"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070520850","display_name":"Jinlong Fei","orcid":"https://orcid.org/0000-0001-8499-9402","observed_orcids":["https://orcid.org/0000-0001-8499-9402","https://orcid.org/0009-0006-0912-9048"]},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinlong Fei","raw_affiliation_strings":["PLA Information Engineering University,School of Cyberspace Security,Zhengzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"PLA Information Engineering University,School of Cyberspace Security,Zhengzhou,China","institution_ids":["https://openalex.org/I169689159"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I169689159"],"apc_list":null,"apc_paid":null,"fwci":1.7365,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.8821005,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1130","last_page":"1134"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.7886000275611877,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.7886000275611877,"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":0.20669999718666077,"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":0.0006000000284984708,"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/intrusion-detection-systems","display_name":"intrusion detection systems","score":0.8939999938011169},{"id":"https://openalex.org/keywords/adversarial-attacks","display_name":"adversarial attacks","score":0.8360000252723694},{"id":"https://openalex.org/keywords/generative-adversarial-networks","display_name":"generative adversarial networks","score":0.9559999704360962},{"id":"https://openalex.org/keywords/traffic-obfuscation","display_name":"traffic obfuscation","score":0.8529999852180481},{"id":"https://openalex.org/keywords/adversarial-examples","display_name":"adversarial examples","score":0.8410000205039978},{"id":"https://openalex.org/keywords/black-box-attacks","display_name":"black-box attacks","score":0.7670000195503235},{"id":"https://openalex.org/keywords/machine-learning-security","display_name":"machine learning security","score":0.5699999928474426},{"id":"https://openalex.org/keywords/denial-of-service-attacks","display_name":"denial-of-service attacks","score":0.6679999828338623},{"id":"https://openalex.org/keywords/probing-attacks","display_name":"probe attacks","score":0.7760000228881836},{"id":"https://openalex.org/keywords/nsl-kdd","display_name":"NSL-KDD dataset","score":0.671999990940094}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.9508262872695923},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7086542248725891},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.6216721534729004},{"id":"https://openalex.org/C158251709","wikidata":"https://www.wikidata.org/wiki/Q354025","display_name":"Intrusion","level":2,"score":0.5216629505157471},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.5167721509933472},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4824681580066681},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.41134849190711975},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3146141767501831},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.13630956411361694},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.07499980926513672},{"id":"https://openalex.org/C17409809","wikidata":"https://www.wikidata.org/wiki/Q161764","display_name":"Geochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvidl62147.2024.10603582","doi":"https://doi.org/10.1109/cvidl62147.2024.10603582","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvidl62147.2024.10603582","pdf_url":null,"source":{"id":"https://openalex.org/S7407086714","display_name":"International Conference on Computer Vision, Image and Deep Learning (CVIDL)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W433644524","https://openalex.org/W1673923490","https://openalex.org/W2516574342","https://openalex.org/W2728257130","https://openalex.org/W2737511514","https://openalex.org/W2853623529","https://openalex.org/W2926701059","https://openalex.org/W2944645079","https://openalex.org/W2963391384","https://openalex.org/W2963448658","https://openalex.org/W2991408690","https://openalex.org/W3009091629","https://openalex.org/W3017825983","https://openalex.org/W3024548636","https://openalex.org/W3102844060","https://openalex.org/W3134622107","https://openalex.org/W3158507034"],"related_works":["https://openalex.org/W3171785101","https://openalex.org/W2889836475","https://openalex.org/W4317931010","https://openalex.org/W4400727500","https://openalex.org/W2963391384","https://openalex.org/W3096929904","https://openalex.org/W4387870999","https://openalex.org/W2802196017","https://openalex.org/W4286005659","https://openalex.org/W4320895574"],"abstract_inverted_index":{"Intrusion":[0],"detection":[1,30,110],"protects":[2],"network":[3,25,87],"security":[4],"and":[5,10,14,78,95,98,124,170],"data":[6],"integrity":[7],"by":[8],"monitoring":[9],"identifying":[11],"malicious":[12,172],"activities":[13],"abnormal":[15],"behaviors":[16],"in":[17,59,76],"the":[18,21,35,66,128,138,140,147,151,161],"network.":[19],"With":[20],"increasing":[22],"volume":[23],"of":[24,68,114,127,150],"data,":[26],"machine":[27,43],"learning-based":[28],"intrusion":[29],"has":[31,40],"gained":[32],"popularity":[33],"as":[34,106],"predominant":[36],"approach.":[37],"However,":[38],"research":[39],"demonstrated":[41],"that":[42,160],"learning":[44],"is":[45,71,88,131],"susceptible":[46],"to":[47,73,102,107,145],"adversarial":[48,69,80,86],"samples,":[49],"where":[50],"even":[51],"a":[52,132],"small,":[53],"intentionally":[54],"crafted":[55],"perturbation":[56,101],"can":[57],"result":[58],"incorrect":[60],"model":[61,109],"outputs.":[62],"In":[63,116,137],"this":[64,118],"paper,":[65],"idea":[67],"samples":[70],"applied":[72],"traffic":[74],"obfuscation":[75,81,135,148,165],"IDS,":[77],"an":[79],"method":[82,91,119,152,162],"based":[83],"on":[84,153],"generative":[85],"proposed.":[89],"This":[90],"clearly":[92],"divides":[93],"functional":[94],"nonfunctional":[96,103],"features,":[97,104],"only":[99],"adds":[100],"so":[105],"avoid":[108],"while":[111],"maintaining":[112],"functionality":[113],"traffic.":[115,173],"addition,":[117],"does":[120],"not":[121],"require":[122],"structure":[123],"internal":[125],"parameters":[126],"model,":[129],"which":[130],"black":[133],"box":[134],"method.":[136],"experiment,":[139],"NSL-KDD":[141],"dataset":[142],"was":[143],"employed":[144],"assess":[146],"performance":[149],"two":[154],"target":[155],"models.":[156],"The":[157],"results":[158],"indicate":[159],"demonstrates":[163],"effective":[164],"capabilities":[166],"for":[167],"both":[168],"DoS":[169],"Probe":[171]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2024-07-27T00:00:00"}