{"id":"W3115138069","doi":"10.1109/isncc49221.2020.9297170","title":"An Effective Hybrid Anomaly Detection System Based on Mixture Models","year":2020,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Anomaly detection; Computer science; Intrusion detection system; Feature selection; Data mining; Variety (cybernetics); Anomaly (physics); Feature (linguistics); Range (aeronautics); Domain (mathematical analysis); Machine learning; Data modeling; Artificial intelligence; Database; Engineering","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":[0.001714633,0.001048394,0.001891062,0.002342388,0.0006115711,0.001287039,0.001958004,0.001091793,0.001041555],"category_scores_gemma":[0.002863742,0.0004732528,0.00123183,0.001394708,0.0004349706,0.002020787,0.001767063,0.001301166,0.0009279202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006017665,"about_ca_system_score_gemma":0.0007284599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002961246,"about_ca_topic_score_gemma":0.002629299,"domain_scores_codex":[0.9984295,0.0002667721,0.0001186445,0.000492489,0.0005590382,0.0001335385],"domain_scores_gemma":[0.9987763,0.0004293645,0.0001061198,0.000152382,0.0004631358,0.00007271193],"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":[0.0008148348,0.0005127254,0.01189986,0.0001344219,0.0004457171,0.0002732738,0.000202418,0.08437692,0.04288257,0.006017855,0.00551155,0.8469279],"study_design_scores_gemma":[0.00001483753,0.00006690136,0.001073872,0.000004268768,0.00004524916,0.0001514576,0.00001536145,0.987758,0.007302452,0.00215102,0.001384354,0.00003221693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02562554,0.0002750799,0.9681647,0.0001695806,0.00009218594,0.00005743137,0.0001157937,0.004945873,0.0005538332],"genre_scores_gemma":[0.5113875,0.0003076693,0.4834917,0.0002692884,0.000156553,0.0001521021,0.0007104127,0.0001743428,0.003350509],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002961246,"threshold_uncertainty_score":0.009067953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008920553674315431,"score_gpt":0.1997437852782281,"score_spread":0.1908232316039126,"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."}}