{"id":"W3009500417","doi":"10.23919/cnsm46954.2019.9012708","title":"Exploring Feature Normalization and Temporal Information for Machine Learning Based Insider Threat Detection","year":2019,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Insider threat; Normalization (sociology); Computer science; Leverage (statistics); Insider; Random forest; Artificial intelligence; Machine learning; Classifier (UML); Pattern recognition (psychology); Computer security; Data mining","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":[0.001928561,0.0006602413,0.0006861257,0.001210414,0.0004110948,0.0007489356,0.0005758799,0.0004476031,0.001391692],"category_scores_gemma":[0.005842403,0.0001630684,0.0007522026,0.002015229,0.0004969704,0.002077049,0.0005572966,0.001021474,0.0005654613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004446323,"about_ca_system_score_gemma":0.0007719499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002010722,"about_ca_topic_score_gemma":0.002182156,"domain_scores_codex":[0.9991239,0.0002828579,0.000077873,0.000175701,0.0002484506,0.00009123528],"domain_scores_gemma":[0.9972914,0.001282127,0.0003262264,0.0005756031,0.0004570591,0.0000675631],"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":[0.0004172356,0.000392002,0.008704552,0.0001633419,0.0001073663,0.0001952947,0.0001898869,0.05749726,0.08117174,0.004927779,0.002483149,0.8437504],"study_design_scores_gemma":[0.00002680505,0.0003689079,0.0162452,0.00004230508,0.00008949788,0.0003181177,0.0002060442,0.9163129,0.04988156,0.01043059,0.006004559,0.00007343453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1423844,0.001305352,0.8512064,0.0005064792,0.0001747868,0.0001165746,0.0003108618,0.002136644,0.001858438],"genre_scores_gemma":[0.7224655,0.0006393194,0.274446,0.0000989784,0.0001225827,0.0001333513,0.0006961322,0.0001424358,0.001255665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002010722,"threshold_uncertainty_score":0.01019937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02602555497228171,"score_gpt":0.2100839888612904,"score_spread":0.1840584338890087,"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."}}