{"id":"W3009354171","doi":"10.1109/access.2020.2977325","title":"PredictDeep: Security Analytics as a Service for Anomaly Detection and Prediction","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Anomaly detection; Analytics; Computer security; Data mining","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":[0.0002161136,0.0001118999,0.0001506976,0.00004394696,0.0001523066,0.0002221789,0.0004998152,0.000094941,0.000002473005],"category_scores_gemma":[0.00006812531,0.00009890907,0.00004359085,0.0004996253,0.0000206259,0.001234144,0.0001270588,0.00009921777,0.00001052852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002922174,"about_ca_system_score_gemma":0.00005233103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001334029,"about_ca_topic_score_gemma":0.00004889788,"domain_scores_codex":[0.9989936,0.00003032736,0.0002253501,0.0003932593,0.0001830205,0.0001743833],"domain_scores_gemma":[0.9992434,0.00006922736,0.00009161513,0.0002765194,0.0001876631,0.0001316035],"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":[0.001246032,0.0007339179,0.6970553,0.01105332,0.0007152435,0.00003978614,0.03766716,0.01062751,0.01664458,0.001853742,0.01266008,0.2097034],"study_design_scores_gemma":[0.000736388,0.0003757777,0.02988045,0.00003385645,0.00003650909,0.00002083475,0.00004073489,0.9482542,0.01472717,0.002135565,0.003560559,0.0001980032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5999768,0.00005037416,0.3976482,0.001073082,0.0004923068,0.0003844736,0.00001479158,0.0002691783,0.00009083768],"genre_scores_gemma":[0.9979062,0.00002138308,0.0005111645,0.001172581,0.0003177028,0.00005593654,0.000002729289,0.000007028054,0.000005266118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9376267,"threshold_uncertainty_score":0.4033395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02814644120326673,"score_gpt":0.2768000049464547,"score_spread":0.2486535637431879,"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."}}