{"id":"W4380031458","doi":"10.1007/978-3-031-35504-2_5","title":"Honey, I Chunked the Passwords: Generating Semantic Honeywords Resistant to Targeted Attacks Using Pre-trained Language Models","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Password; Computer science; Leverage (statistics); Metric (unit); Exploit; Computer security; Information retrieval; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004486858,0.0009223326,0.0005978713,0.0004648438,0.0003644582,0.0006278202,0.0006419852,0.0008363401,0.004886412],"category_scores_gemma":[0.001847158,0.0003883087,0.0007467332,0.0002427993,0.0006062167,0.001866221,0.001230878,0.001340396,0.004021533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002642086,"about_ca_system_score_gemma":0.0004834549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007517894,"about_ca_topic_score_gemma":0.001261765,"domain_scores_codex":[0.9997345,0.00005382299,0.00002100124,0.00009886375,0.00005824151,0.00003364045],"domain_scores_gemma":[0.9993227,0.0002127292,0.0000439963,0.000232222,0.0001510802,0.00003734451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001048879,0.000210632,0.001131762,0.0004613657,0.0001564335,0.0004628494,0.0004085614,0.03967065,0.1474114,0.01538909,0.03087852,0.7627699],"study_design_scores_gemma":[0.0001142368,0.000765155,0.001002464,0.00007211067,0.000155762,0.0007829421,0.000261986,0.7758476,0.1669277,0.03183043,0.02213595,0.0001035995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07693988,0.0008101233,0.8965248,0.0005386165,0.0007802197,0.0002455315,0.001071118,0.01656799,0.006521727],"genre_scores_gemma":[0.4871278,0.0004905484,0.4864084,0.0005181174,0.000163071,0.0002413628,0.003072062,0.00203904,0.01993959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004886412,"threshold_uncertainty_score":0.01634669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03079896664455443,"score_gpt":0.277328930986188,"score_spread":0.2465299643416336,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}