{"id":"W2954155127","doi":"10.1109/sege.2019.8859946","title":"Smart Grid Cyber Attacks Detection Using Supervised Learning and Heuristic Feature Selection","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Heuristic; Computer science; Feature selection; Machine learning; Artificial intelligence; Feature (linguistics); Data mining; Grid; Smart grid; Supervised learning; Selection (genetic algorithm); Pattern recognition (psychology); Engineering; Artificial neural network; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001407378,0.0003059463,0.0002924323,0.0001635591,0.0001576032,0.0001356157,0.00009431969,0.0005693039,0.00007351094],"category_scores_gemma":[0.00003520136,0.0003034496,0.00008194025,0.0001313726,0.00003172882,0.0001065046,0.0001514056,0.001542884,0.00003524965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001350216,"about_ca_system_score_gemma":0.00003048057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000141511,"about_ca_topic_score_gemma":0.0001638901,"domain_scores_codex":[0.9988815,0.00005897777,0.0001816676,0.0004072215,0.0001818489,0.0002887865],"domain_scores_gemma":[0.9996005,0.00005799063,0.00004173226,0.0001586549,0.00006133305,0.00007980865],"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":[0.00002440924,0.00001669556,0.02457257,0.001254295,0.0001215467,0.000006977436,0.00053954,0.9468428,0.02319253,0.000008905162,0.0006010069,0.002818724],"study_design_scores_gemma":[0.0002066174,0.00003233878,0.008741735,0.0001679271,0.00006714889,0.00007209063,0.000085117,0.978207,0.006250206,0.0000293252,0.005658375,0.0004821036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9656643,0.0008994165,0.02699827,0.00002108998,0.003750882,0.0003341095,0.000004928551,0.0006019793,0.001725063],"genre_scores_gemma":[0.9972622,0.0004028731,0.0009429981,0.00001593321,0.0006375512,0.000010545,0.00002631563,0.00005620596,0.0006453375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03159798,"threshold_uncertainty_score":0.9999418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009941455604182492,"score_gpt":0.2278778356364674,"score_spread":0.2179363800322849,"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."}}