{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009679635,0.0005421403,0.0006055029,0.001007529,0.0002023076,0.0005635416,0.0003469139,0.0003504206,0.0003522774],"category_scores_gemma":[0.002241503,0.000125716,0.0004842985,0.000511301,0.0003784807,0.0005569858,0.0002411949,0.000270132,0.00008483401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005021993,"about_ca_system_score_gemma":0.0005198283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002222168,"about_ca_topic_score_gemma":0.001720238,"domain_scores_codex":[0.9994159,0.0002188077,0.00003692548,0.00009406332,0.0001696249,0.00006466795],"domain_scores_gemma":[0.9983217,0.0008974001,0.0002583009,0.0001400691,0.0003431249,0.00003943958],"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":[0.0005292315,0.0004383069,0.01249752,0.0000980629,0.0001781229,0.0001322962,0.00009104615,0.5538606,0.01011741,0.001786433,0.001182396,0.4190885],"study_design_scores_gemma":[0.000008526848,0.00008682286,0.001821698,0.000002743016,0.00001126501,0.00002390709,0.00001134695,0.994891,0.002527374,0.0005238396,0.0000864917,0.000004940277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4742559,0.0003327208,0.5224338,0.0002205859,0.00003798705,0.00009232523,0.0001004775,0.0008629035,0.00166338],"genre_scores_gemma":[0.9585533,0.00005367025,0.04084088,0.00002471132,0.00001901324,0.00003817904,0.00009500093,0.00001240786,0.000362893],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002222168,"threshold_uncertainty_score":0.005119145,"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."}}