{"id":"W4405386894","doi":"10.1007/978-3-031-80020-7_10","title":"From Traits to Threats: Learning Risk Indicators of Malicious Insider Using Psychometric Data","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Information and Cyber Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Insider threat; Computer security; Insider; 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":[0.006936703,0.001271075,0.0007255048,0.002696819,0.00037166,0.002838674,0.001035301,0.001098088,0.001496911],"category_scores_gemma":[0.05079732,0.0004505633,0.001208587,0.002196672,0.0007391953,0.003293299,0.001537411,0.003319833,0.001174761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004482199,"about_ca_system_score_gemma":0.0005591063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002182854,"about_ca_topic_score_gemma":0.002701127,"domain_scores_codex":[0.9974642,0.00140709,0.0001745799,0.0003626986,0.0004845392,0.0001069543],"domain_scores_gemma":[0.9460775,0.0449779,0.003024844,0.002812451,0.002283038,0.0008242713],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003165905,0.0006540925,0.7734272,0.0001139444,0.0004909387,0.00009310387,0.001101804,0.01253223,0.001284108,0.001888059,0.003419735,0.2046782],"study_design_scores_gemma":[0.00004920679,0.0009735316,0.5897765,0.0003093887,0.0003464551,0.0003054913,0.002865913,0.3656189,0.003389488,0.03411007,0.002082365,0.0001726936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8874381,0.000472973,0.106289,0.0004475381,0.00008696875,0.0001470415,0.001494252,0.0005709366,0.003053259],"genre_scores_gemma":[0.9710306,0.000227858,0.02611942,0.00004496476,0.00002570855,0.0001260178,0.001566439,0.00007116443,0.000787851],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006936703,"threshold_uncertainty_score":0.03668523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03492758816241651,"score_gpt":0.2920353399742763,"score_spread":0.2571077518118599,"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."}}