{"id":"https://openalex.org/W4415195148","doi":"https://doi.org/10.1609/aies.v8i3.36698","title":"You Don\u2019t Need Robust Machine Learning to Manage Adversarial Attack Risks","display_name":"You Don\u2019t Need Robust Machine Learning to Manage Adversarial Attack Risks","publication_year":2025,"publication_date":"2025-10-15","ids":{"openalex":"https://openalex.org/W4415195148","doi":"https://doi.org/10.1609/aies.v8i3.36698"},"language":"en","primary_location":{"id":"doi:10.1609/aies.v8i3.36698","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aies.v8i3.36698","pdf_url":"https://ojs.aaai.org/index.php/AIES/article/download/36698/38836","source":{"id":"https://openalex.org/S5407048695","display_name":"Proceedings of the AAAI/ACM Conference on AI Ethics and Society","issn_l":"3065-8365","issn":["3065-8365"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://ojs.aaai.org/index.php/AIES/article/download/36698/38836","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068036546","display_name":"Edward Raff","orcid":"https://orcid.org/0000-0002-9900-1972"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Edward Raff","raw_affiliation_strings":["CrowdStrike"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CrowdStrike","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036782206","display_name":"Michel Benaroch","orcid":"https://orcid.org/0000-0002-8605-8814"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michel Benaroch","raw_affiliation_strings":["Syracuse University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018573642","display_name":"Andrew Farris","orcid":"https://orcid.org/0000-0003-2023-9636"},"institutions":[{"id":"https://openalex.org/I1322124587","display_name":"Booz Allen Hamilton (United States)","ror":"https://ror.org/051rcp357","country_code":"US","type":"company","lineage":["https://openalex.org/I1322124587"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrew L. Farris","raw_affiliation_strings":["Booz Allen Hamiltion"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Booz Allen Hamiltion","institution_ids":["https://openalex.org/I1322124587"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6768,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.86027738,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"8","issue":"3","first_page":"2094","last_page":"2106"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9937000274658203,"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":0.9937000274658203,"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":0.9794999957084656,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9484000205993652,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7483000159263611},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7106999754905701},{"id":"https://openalex.org/keywords/warrant","display_name":"Warrant","score":0.6636999845504761},{"id":"https://openalex.org/keywords/threat-model","display_name":"Threat model","score":0.633899986743927},{"id":"https://openalex.org/keywords/adversarial-machine-learning","display_name":"Adversarial machine learning","score":0.49059998989105225}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7483000159263611},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7106999754905701},{"id":"https://openalex.org/C2776916960","wikidata":"https://www.wikidata.org/wiki/Q637156","display_name":"Warrant","level":2,"score":0.6636999845504761},{"id":"https://openalex.org/C140547941","wikidata":"https://www.wikidata.org/wiki/Q7797194","display_name":"Threat model","level":2,"score":0.633899986743927},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6248000264167786},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.5827000141143799},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5521000027656555},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4959999918937683},{"id":"https://openalex.org/C2778403875","wikidata":"https://www.wikidata.org/wiki/Q20312394","display_name":"Adversarial machine learning","level":3,"score":0.49059998989105225},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.421999990940094},{"id":"https://openalex.org/C65856478","wikidata":"https://www.wikidata.org/wiki/Q3991682","display_name":"Attack model","level":2,"score":0.34779998660087585},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aies.v8i3.36698","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aies.v8i3.36698","pdf_url":"https://ojs.aaai.org/index.php/AIES/article/download/36698/38836","source":{"id":"https://openalex.org/S5407048695","display_name":"Proceedings of the AAAI/ACM Conference on AI Ethics and Society","issn_l":"3065-8365","issn":["3065-8365"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aies.v8i3.36698","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aies.v8i3.36698","pdf_url":"https://ojs.aaai.org/index.php/AIES/article/download/36698/38836","source":{"id":"https://openalex.org/S5407048695","display_name":"Proceedings of the AAAI/ACM Conference on AI Ethics and Society","issn_l":"3065-8365","issn":["3065-8365"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415195148.pdf","grobid_xml":"https://content.openalex.org/works/W4415195148.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,16],"robustness":[1,163],"of":[2,38,88,162,169,173,236],"modern":[3],"machine":[4,102],"learning":[5,103],"(ML)":[6],"models":[7,42,98,196,237],"has":[8],"become":[9],"an":[10,122,183],"increasing":[11],"concern":[12],"within":[13],"the":[14,62,78,108,134,158,191,230,234],"community.":[15],"ability":[17],"to":[18,30,44,100,165,190,209,217,222],"subvert":[19,77],"a":[20,56,86,166,200,206,226,239],"model":[21],"into":[22],"making":[23],"errant":[24],"predictions":[25],"using":[26],"seemingly":[27],"inconsequential":[28],"changes":[29],"input":[31],"is":[32,35,119],"startling,":[33],"as":[34,205,225],"our":[36,111],"lack":[37],"success":[39],"in":[40,113,132,232,238],"building":[41],"robust":[43,99,194],"this":[45,81],"concern.":[46],"Existing":[47],"research":[48,216],"shows":[49],"progress,":[50],"but":[51],"current":[52],"mitigations":[53],"come":[54],"with":[55,121,219],"high":[57],"cost":[58,159],"and":[59,110,139,160],"simultaneously":[60],"reduce":[61],"model's":[63],"accuracy.":[64],"However,":[65],"such":[66,116],"trade-offs":[67,161],"may":[68,91],"not":[69,92,156],"be":[70,144,186],"necessary":[71,198],"when":[72],"other":[73],"design":[74,211],"choices":[75],"could":[76,143],"risk.":[79],"In":[80,146],"article,":[82],"we":[83,149,204],"argue":[84],"that":[85,151],"majority":[87],"real-world":[89,223],"applications":[90],"have":[93],"any":[94],"immediate":[95],"need":[96,208],"for":[97,136,199],"adversarial":[101],"(AML)":[104],"by":[105],"critically":[106],"analyzing":[107],"literature,":[109],"experience":[112],"designing":[114],"around":[115],"constraints.":[117],"This":[118],"done":[120],"eye":[123],"toward":[124],"how":[125,140],"one":[126],"would":[127],"then":[128],"mitigate":[129],"these":[130],"attacks":[131],"practice,":[133],"risks":[135,142],"production":[137],"deployment,":[138],"those":[141],"managed.":[145],"doing":[147],"so":[148],"elucidate":[150],"many":[152],"AML":[153,189],"threats":[154],"do":[155],"warrant":[157],"due":[164],"low":[167],"likelihood":[168],"attack":[170],"or":[171],"availability":[172],"superior":[174],"non-ML":[175],"mitigation.":[176],"Our":[177],"analysis":[178],"also":[179],"recommends":[180],"cases":[181],"where":[182,193],"actor":[184],"should":[185],"concerned":[187],"about":[188],"degree":[192],"ML":[195],"are":[197],"complete":[201],"deployment.":[202],"Ultimately,":[203],"community":[207],"better":[210],"benchmark":[212],"threat":[213,241],"modelsthat":[214],"allow":[215],"progress":[218],"more":[220],"applicability":[221],"use,":[224],"direction":[227],"separate":[228],"from":[229],"value":[231],"understanding":[233],"abilities/limitations":[235],"``lab''":[240],"model.":[242]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-15T00:00:00"}
