{"id":"https://openalex.org/W4390189667","doi":"https://doi.org/10.1109/milcom58377.2023.10356229","title":"DUBIOUS: Detecting Unknown Backdoored Input by Observing Unusual Signatures","display_name":"DUBIOUS: Detecting Unknown Backdoored Input by Observing Unusual Signatures","publication_year":2023,"publication_date":"2023-10-30","ids":{"openalex":"https://openalex.org/W4390189667","doi":"https://doi.org/10.1109/milcom58377.2023.10356229"},"language":"en","primary_location":{"id":"doi:10.1109/milcom58377.2023.10356229","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/milcom58377.2023.10356229","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM)","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/A5104219519","display_name":"Matthew Yudin","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Matthew Yudin","raw_affiliation_strings":["Peraton Labs Inc.,NJ,USA","Peraton Labs Inc., NJ, USA"],"affiliations":[{"raw_affiliation_string":"Peraton Labs Inc.,NJ,USA","institution_ids":[]},{"raw_affiliation_string":"Peraton Labs Inc., NJ, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078482977","display_name":"Rauf Izmailov","orcid":"https://orcid.org/0000-0002-7326-669X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rauf Izmailov","raw_affiliation_strings":["Peraton Labs Inc.,NJ,USA","Peraton Labs Inc., NJ, USA"],"affiliations":[{"raw_affiliation_string":"Peraton Labs Inc.,NJ,USA","institution_ids":[]},{"raw_affiliation_string":"Peraton Labs Inc., NJ, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5104219519"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1746,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.59061614,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"7","issue":null,"first_page":"696","last_page":"702"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994999766349792,"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":0.9994999766349792,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9994000196456909,"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":0.9958999752998352,"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/computer-science","display_name":"Computer science","score":0.5654264688491821},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43886950612068176}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5654264688491821},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43886950612068176}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/milcom58377.2023.10356229","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/milcom58377.2023.10356229","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM)","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":19,"referenced_works":["https://openalex.org/W2082190528","https://openalex.org/W2350778671","https://openalex.org/W2807363941","https://openalex.org/W2898759955","https://openalex.org/W2934843808","https://openalex.org/W2942091739","https://openalex.org/W2966104011","https://openalex.org/W2990270730","https://openalex.org/W3101143027","https://openalex.org/W3109409894","https://openalex.org/W3114686421","https://openalex.org/W3128663834","https://openalex.org/W3207360435","https://openalex.org/W4226026565","https://openalex.org/W4283789713","https://openalex.org/W4289300166","https://openalex.org/W6756074407","https://openalex.org/W6756333562","https://openalex.org/W6787959460"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032","https://openalex.org/W2382290278"],"abstract_inverted_index":{"Trojan":[0,64,95,195],"/":[1],"backdoor":[2],"attacks":[3,79],"represent":[4],"a":[5,33,55,134,152],"serious":[6],"threat":[7],"when":[8],"using":[9,198],"machine":[10],"learning":[11],"models":[12],"that":[13,25,105],"are":[14,130],"either":[15],"supplied":[16],"by":[17,110],"unreliable":[18],"3rd":[19],"parties,":[20],"or":[21],"trained":[22],"on":[23,42,122],"data":[24],"contain":[26],"deliberately":[27],"poisoned":[28],"samples.":[29],"For":[30],"such":[31],"situations,":[32],"malicious":[34],"actor":[35],"can":[36,177],"control":[37],"outputs":[38,124],"of":[39,125,171],"the":[40,74,101,123,148,163,172,185],"model":[41,56],"specially":[43],"crafted":[44],"inputs.":[45],"It":[46],"is":[47,86,93,139],"not":[48],"yet":[49],"possible":[50],"to":[51,77,88,132,145,169,182],"accurately":[52],"detect":[53],"if":[54,90,98,184],"was":[57,187],"poisoned,":[58],"so":[59],"security":[60],"considerations":[61],"require":[62],"augmenting":[63],"detection":[65,181],"methods":[66],"with":[67],"mitigation":[68,197],"strategies.":[69],"Mitigation":[70],"measures":[71],"should":[72],"reduce":[73],"model\u2019s":[75,102],"vulnerability":[76],"poisoning":[78],"while":[80],"maintaining":[81],"its":[82,160],"accuracy.":[83],"Our":[84],"goal":[85],"thus":[87],"determine":[89,183],"an":[91],"input":[92,186],"triggering":[94],"behavior,":[96],"and":[97,117,166,210],"so,":[99],"reject":[100],"prediction":[103],"for":[104],"input.":[106],"We":[107,176],"do":[108],"this":[109,191],"first":[111],"perturbing":[112],"clean":[113,174],"samples":[114],"multiple":[115,142,202],"times,":[116],"then":[118,178],"gathering":[119],"special":[120],"statistics":[121,129],"these":[126],"perturbations.":[127],"These":[128],"referred":[131],"as":[133],"sample\u2019s":[135],"signature.":[136],"The":[137],"signature":[138,161],"built":[140],"across":[141],"perturbation":[143],"magnitudes":[144],"better":[146],"capture":[147],"perturbations\u2019":[149],"effects.":[150],"Given":[151],"novel":[153],"sample":[154],"at":[155],"test":[156],"time,":[157],"we":[158,193],"build":[159],"in":[162,201],"same":[164],"way,":[165],"compare":[167],"it":[168],"those":[170],"known":[173],"examples.":[175],"apply":[179],"outlier":[180],"indeed":[188],"poisoned.":[189],"In":[190],"paper,":[192],"examine":[194],"attack":[196],"standard":[199],"datasets":[200],"modalities,":[203],"namely":[204],"image":[205],"classification,":[206],"natural":[207],"language":[208],"processing,":[209],"malware":[211],"detection.":[212]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}
