{"id":"W3047980200","doi":"10.1109/cns48642.2020.9162254","title":"Exploring Adversarial Properties of Insider Threat Detection","year":2020,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Insider threat; Insider; Adversarial system; Computer science; Robustness (evolution); Anomaly detection; Government (linguistics); Isolation (microbiology); Artificial intelligence; Computer security; Machine learning; Data mining; Political science","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":[],"consensus_categories":[],"category_scores_codex":[0.00005724184,0.00005679829,0.0000820808,0.00003253177,0.00006169108,0.00002945266,0.0001724692,0.00002506211,0.0000210024],"category_scores_gemma":[0.00003003064,0.00004574667,0.00003840147,0.000271234,0.00001779696,0.0009494321,0.0001278792,0.00007459502,0.00002693311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001037436,"about_ca_system_score_gemma":0.0000127652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007490403,"about_ca_topic_score_gemma":0.00001958132,"domain_scores_codex":[0.9994465,0.00002696298,0.000135407,0.0001616272,0.0001412175,0.00008831667],"domain_scores_gemma":[0.9997367,0.000008704569,0.00003772854,0.0001265287,0.00004514887,0.00004522417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000212892,0.00006894446,0.0001209918,0.0000944536,0.00004416291,0.000005241365,0.01245166,0.001718054,0.6003063,0.02074206,0.0002599765,0.3639753],"study_design_scores_gemma":[0.0002063687,0.0001839562,0.0001330165,0.00001322029,0.000002825307,0.000002898503,0.00009990244,0.1012476,0.8956599,0.0003255988,0.002040613,0.00008408808],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6450768,0.00005877006,0.3517739,0.0008961239,0.0006582685,0.0001000262,8.466883e-8,0.0002293881,0.001206733],"genre_scores_gemma":[0.9980676,0.00003672029,0.001441455,0.0002426217,0.0001883422,0.000007829185,5.381979e-8,0.000003057553,0.00001229533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3638912,"threshold_uncertainty_score":0.1865495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1542043214229329,"score_gpt":0.2142944332820047,"score_spread":0.06009011185907184,"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."}}