{"id":"https://openalex.org/W4408354830","doi":"https://doi.org/10.1109/icassp49660.2025.10888919","title":"Targeted Password Guessing Using Neural Language Models","display_name":"Targeted Password Guessing Using Neural Language Models","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408354830","doi":"https://doi.org/10.1109/icassp49660.2025.10888919"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10888919","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10888919","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5101844853","display_name":"Jiahong Yang","orcid":"https://orcid.org/0009-0002-2029-044X"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiahong Yang","raw_affiliation_strings":["Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115602687","display_name":"Wenting Li","orcid":"https://orcid.org/0000-0002-1411-0572"},"institutions":[{"id":"https://openalex.org/I4210135483","display_name":"Beijing Institute of Graphic Communication","ror":"https://ror.org/03yg3v757","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135483"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenting Li","raw_affiliation_strings":["Beijing Institute of Graphic Communication"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Graphic Communication","institution_ids":["https://openalex.org/I4210135483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073639015","display_name":"Haibo Cheng","orcid":"https://orcid.org/0000-0001-6677-463X"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haibo Cheng","raw_affiliation_strings":["Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100338689","display_name":"Ping Wang","orcid":"https://orcid.org/0000-0002-8854-2079"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Wang","raw_affiliation_strings":["Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101844853"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":12.3112,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.98034146,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11800","display_name":"User Authentication and Security Systems","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T11800","display_name":"User Authentication and Security Systems","score":0.9997000098228455,"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"}},{"id":"https://openalex.org/T11241","display_name":"Advanced Malware Detection Techniques","score":0.960099995136261,"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/T10828","display_name":"Biometric Identification and Security","score":0.9190000295639038,"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":0.7731914520263672},{"id":"https://openalex.org/keywords/password","display_name":"Password","score":0.728939950466156},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.41465556621551514},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41031455993652344},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.27265939116477966}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7731914520263672},{"id":"https://openalex.org/C109297577","wikidata":"https://www.wikidata.org/wiki/Q161157","display_name":"Password","level":2,"score":0.728939950466156},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.41465556621551514},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41031455993652344},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.27265939116477966}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10888919","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10888919","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320324787","display_name":"Peking University","ror":"https://ror.org/02v51f717"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2007488200","https://openalex.org/W2064675550","https://openalex.org/W2073342447","https://openalex.org/W2135359429","https://openalex.org/W2157151879","https://openalex.org/W2463456957","https://openalex.org/W2490171383","https://openalex.org/W2538793708","https://openalex.org/W2931153881","https://openalex.org/W4225308903","https://openalex.org/W4372267478","https://openalex.org/W4384024451","https://openalex.org/W4385245566","https://openalex.org/W4387889547","https://openalex.org/W6640212811","https://openalex.org/W6757817989","https://openalex.org/W6800807472"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W143386018","https://openalex.org/W2656445685","https://openalex.org/W1004582678","https://openalex.org/W2115218409","https://openalex.org/W2090949570","https://openalex.org/W2263162522","https://openalex.org/W3204019825"],"abstract_inverted_index":{"With":[0],"the":[1,40,147,155],"increasing":[2],"prevalence":[3],"of":[4,42,91,129,149,157],"personal":[5],"information":[6],"breaches,":[7],"targeted":[8,23,77,105,158],"password":[9,24,43,78,106,113,159],"guessing":[10,25,79,160],"based":[11],"on":[12,29,110],"user-specific":[13],"data":[14],"has":[15],"emerged":[16],"as":[17],"a":[18,88],"serious":[19],"security":[20],"threat.":[21],"Existing":[22],"attacks":[26],"primarily":[27],"rely":[28],"traditional":[30],"statistical":[31],"language":[32,52,63,96,118,151],"models,":[33,53],"which":[34],"have":[35,57],"limited":[36],"capability":[37],"in":[38,50,61,104],"addressing":[39],"complexities":[41],"structures":[44],"and":[45,70,100,135],"user":[46],"behavior.":[47],"Recent":[48],"advancements":[49],"neural":[51,95,117,150],"particularly":[54],"Transformer-based":[55],"architectures,":[56],"achieved":[58],"significant":[59],"success":[60],"natural":[62],"processing":[64],"tasks":[65],"by":[66],"capturing":[67],"complex":[68],"patterns":[69],"dependencies.":[71],"However,":[72],"their":[73,102],"potential":[74,148],"for":[75],"improving":[76],"remains":[80],"largely":[81],"unexplored.To":[82],"address":[83],"this":[84],"gap,":[85],"we":[86],"conduct":[87],"systematic":[89],"evaluation":[90],"several":[92],"widely":[93],"used":[94],"models":[97,119,125,152],"from":[98],"NLP":[99],"assess":[101],"effectiveness":[103,156],"guessing.":[107],"Experimental":[108],"results":[109],"multiple":[111],"real-world":[112],"datasets":[114],"show":[115],"that":[116],"outperform":[120],"existing":[121],"approaches.":[122],"Our":[123],"proposed":[124],"achieve":[126],"an":[127],"improvement":[128],"1.4%\u20134.6%":[130],"compared":[131,137],"to":[132,138,153],"RFGuess-PII":[133],"model,":[134],"18%\u201340%":[136],"TarPCFG":[139],"model.":[140],"This":[141],"work":[142],"provides":[143],"new":[144],"insights":[145],"into":[146],"enhance":[154],"attacks.":[161]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}