{"id":"https://openalex.org/W4398157294","doi":"https://doi.org/10.1145/3637528.3671910","title":"Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective","display_name":"Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4398157294","doi":"https://doi.org/10.1145/3637528.3671910"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671910","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671910","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2405.10757","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zhiwei Zhang","orcid":"https://orcid.org/0009-0007-6153-2739"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhiwei Zhang","raw_affiliation_strings":["The Pennsylvania State University, State College, PA, USA"],"raw_orcid":"https://orcid.org/0009-0007-6153-2739","affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, State College, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Minhua Lin","orcid":"https://orcid.org/0000-0003-1591-7172"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Minhua Lin","raw_affiliation_strings":["The Pennsylvania State University, State College, PA, USA"],"raw_orcid":"https://orcid.org/0000-0003-1591-7172","affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, State College, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091395218","display_name":"Enyan Dai","orcid":"https://orcid.org/0000-0001-9715-0280"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Enyan Dai","raw_affiliation_strings":["The Pennsylvania State University, State College, PA, USA"],"raw_orcid":"https://orcid.org/0000-0001-9715-0280","affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, State College, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011048500","display_name":"Suhang Wang","orcid":"https://orcid.org/0000-0003-3448-4878"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suhang Wang","raw_affiliation_strings":["The Pennsylvania State University, State College, PA, USA"],"raw_orcid":"https://orcid.org/0000-0003-3448-4878","affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, State College, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I130769515"],"apc_list":null,"apc_paid":null,"fwci":12.8147,"has_fulltext":true,"cited_by_count":23,"citation_normalized_percentile":{"value":0.99366657,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"4386","last_page":"4397"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9966999888420105,"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":0.9966999888420105,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9908000230789185,"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"}},{"id":"https://openalex.org/T10734","display_name":"Information and Cyber Security","score":0.9872999787330627,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/backdoor","display_name":"Backdoor","score":0.984189510345459},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.6098573207855225},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5090181827545166},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4493754208087921},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.3605383038520813},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.28381919860839844},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2040618360042572}],"concepts":[{"id":"https://openalex.org/C2781045450","wikidata":"https://www.wikidata.org/wiki/Q254569","display_name":"Backdoor","level":2,"score":0.984189510345459},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.6098573207855225},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5090181827545166},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4493754208087921},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3605383038520813},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28381919860839844},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2040618360042572}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3637528.3671910","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671910","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2405.10757","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.10757","pdf_url":"https://arxiv.org/pdf/2405.10757","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2405.10757","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.10757","pdf_url":"https://arxiv.org/pdf/2405.10757","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G125128811","display_name":null,"funder_award_id":"W911NF-2110198","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G1432373144","display_name":null,"funder_award_id":"W911NF-21-1-","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G7452299184","display_name":null,"funder_award_id":"W911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"}],"funders":[{"id":"https://openalex.org/F4320306110","display_name":"U.S. Department of Homeland Security","ror":"https://ror.org/00jyr0d86"},{"id":"https://openalex.org/F4320307791","display_name":"Cisco Systems","ror":"https://ror.org/03yt1ez60"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4398157294.pdf","grobid_xml":"https://content.openalex.org/works/W4398157294.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W2084512860","https://openalex.org/W2153959628","https://openalex.org/W2790344751","https://openalex.org/W2914721378","https://openalex.org/W2931367569","https://openalex.org/W2942091739","https://openalex.org/W2963893312","https://openalex.org/W3012846134","https://openalex.org/W3015799890","https://openalex.org/W3081178496","https://openalex.org/W3093664513","https://openalex.org/W3117178429","https://openalex.org/W3134509497","https://openalex.org/W3167334189","https://openalex.org/W3206604724","https://openalex.org/W3211600423","https://openalex.org/W3215430231","https://openalex.org/W3217045679","https://openalex.org/W4206776774","https://openalex.org/W4212890525","https://openalex.org/W4214680449","https://openalex.org/W4285066127","https://openalex.org/W4306813287","https://openalex.org/W4318812135","https://openalex.org/W4323066547","https://openalex.org/W6784694379"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W4320031223","https://openalex.org/W4200629851","https://openalex.org/W4281902577","https://openalex.org/W4309417370","https://openalex.org/W4292107232","https://openalex.org/W3009072493","https://openalex.org/W4386080799","https://openalex.org/W3140988292"],"abstract_inverted_index":{"Graph":[0],"Neural":[1],"Networks":[2],"(GNNs)":[3],"have":[4],"shown":[5],"remarkable":[6],"performance":[7],"in":[8,42,109,113,139,194,196],"various":[9,202],"tasks.":[10],"However,":[11],"recent":[12],"works":[13],"reveal":[14],"that":[15,75,199],"GNNs":[16],"are":[17],"vulnerable":[18],"to":[19,37,57,64,82,146,170],"backdoor":[20,23,30,124],"attacks.":[21],"Generally,":[22],"attack":[24,159,209],"poisons":[25],"the":[26,33,43,50,65,90,148,151,175,188,191],"graph":[27,52,123],"by":[28,78,174],"attaching":[29],"triggers":[31,76,96,152,198],"and":[32,101],"target":[34,66],"class":[35],"label":[36],"a":[38,118,157,207],"set":[39],"of":[40,121,150,190],"nodes":[41,60],"training":[44],"graph.":[45,181],"A":[46],"GNN":[47],"trained":[48,178],"on":[49,179,184],"poisoned":[51,180],"will":[53],"then":[54],"be":[55,83,98],"misled":[56],"predict":[58],"test":[59],"attached":[61],"with":[62,103,126,141,162],"trigger":[63,172],"class.":[67],"Despite":[68],"their":[69],"effectiveness,":[70],"our":[71],"empirical":[72],"analysis":[73],"shows":[74],"generated":[77],"existing":[79],"methods":[80,108],"tend":[81],"out-of-distribution":[84],"(OOD),":[85],"which":[86],"significantly":[87],"differ":[88],"from":[89],"clean":[91],"data.":[92],"Hence,":[93],"these":[94],"injected":[95],"can":[97,200],"easily":[99],"detected":[100],"pruned":[102],"widely":[104],"used":[105],"outlier":[106],"detection":[107],"real-world":[110,185],"applications.":[111],"Therefore,":[112],"this":[114],"paper,":[115],"we":[116,134,165],"study":[117],"novel":[119,167],"problem":[120],"unnoticeable":[122],"attacks":[125],"in-distribution":[127],"(ID)":[128],"triggers.":[129],"To":[130,155],"generate":[131,147],"ID":[132,163],"triggers,":[133,164],"introduce":[135,166],"an":[136,142],"OOD":[137],"detector":[138],"conjunction":[140],"adversarial":[143],"learning":[144],"strategy":[145],"attributes":[149],"within":[153],"distribution.":[154],"ensure":[156],"high":[158,208],"success":[160,210],"rate":[161],"modules":[168],"designed":[169],"enhance":[171],"memorization":[173],"victim":[176],"model":[177],"Extensive":[182],"experiments":[183],"datasets":[186],"demonstrate":[187],"effectiveness":[189],"proposed":[192],"method":[193],"generating":[195],"distribution":[197],"bypass":[201],"defense":[203],"strategies":[204],"while":[205],"maintaining":[206],"rate.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":2}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}