{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001858417,0.0006219355,0.0006466365,0.0008371588,0.0006614635,0.001318194,0.004362474,0.0002935591,0.000008873805],"category_scores_gemma":[0.0001852817,0.0004826601,0.0001900489,0.00164211,0.0003394286,0.0005282659,0.001695173,0.0007470571,0.00005467529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003110105,"about_ca_system_score_gemma":0.0005942509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002180804,"about_ca_topic_score_gemma":0.0005745774,"domain_scores_codex":[0.9947826,0.0001485833,0.0008690832,0.001720476,0.001562826,0.0009164503],"domain_scores_gemma":[0.9962496,0.0005053181,0.0003823875,0.002287006,0.0003073978,0.000268255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004307262,0.0001178867,0.00005748522,0.0003722039,0.0001563685,0.0004033068,0.3281569,0.4366004,0.04186591,0.03846504,0.0003303149,0.1534311],"study_design_scores_gemma":[0.0001786046,0.00007147998,0.0000768496,0.0005018665,0.00001405896,0.00002856672,0.000005235006,0.9768438,0.0007567806,0.02076418,0.0001466303,0.0006118961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006303355,0.0002760697,0.9875562,0.002062629,0.002138962,0.001008714,0.00001660887,0.0004243729,0.0002131375],"genre_scores_gemma":[0.8922424,0.00001265684,0.1030117,0.002254836,0.0008008471,0.00003569979,0.00001336702,0.00009150401,0.001536998],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8859391,"threshold_uncertainty_score":0.9997625,"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."}}