{"id":"W3000429356","doi":"10.1109/tnsm.2020.2967721","title":"Analyzing Data Granularity Levels for Insider Threat Detection Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":173,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Insider threat; Insider; Computer science; Granularity; Computer security; Machine learning; Artificial intelligence; Set (abstract data type)","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003604637,0.0002007253,0.0002022832,0.00008992053,0.0009305955,0.0002155376,0.0004755216,0.00007986039,0.00001422124],"category_scores_gemma":[0.000001944373,0.0002074867,0.00006179218,0.0008480821,0.0000154912,0.0006239984,0.00004657615,0.0003137269,0.000006156814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003480542,"about_ca_system_score_gemma":0.00000998397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001544841,"about_ca_topic_score_gemma":0.0007560777,"domain_scores_codex":[0.9984735,0.0001052237,0.0002656469,0.000658527,0.0001873232,0.000309732],"domain_scores_gemma":[0.9991858,0.00007335954,0.00009125564,0.000467767,0.00005662739,0.0001251682],"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":[0.0001471063,0.00007351868,0.00003808005,0.0002383725,0.0002113783,0.000008552338,0.0005281073,0.6343278,0.0003650119,0.000559503,0.00004626591,0.3634563],"study_design_scores_gemma":[0.0005457303,0.0001406691,0.00008893642,0.00004091424,0.0001108942,0.000006306644,0.00003916748,0.9894972,0.0006054146,0.0005579616,0.008145615,0.0002211799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009971309,0.0001531263,0.9870645,0.001532659,0.0005254814,0.0004380041,0.00001042104,0.0002263246,0.00007820046],"genre_scores_gemma":[0.9682661,0.0004615512,0.02859908,0.002355729,0.0002408713,0.00002455441,0.000009574431,0.00002112832,0.00002143033],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9584654,"threshold_uncertainty_score":0.8461062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07647239340849372,"score_gpt":0.2690621962632918,"score_spread":0.1925898028547981,"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."}}