{"id":"W4288286523","doi":"10.48550/arxiv.1907.03313","title":"Smart Grid Cyber Attacks Detection using Supervised Learning and\\n Heuristic Feature Selection","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Heuristic; Feature selection; Machine learning; Artificial intelligence; Data mining; Grid; Smart grid; Feature (linguistics); Supervised learning; Cyber-attack; Selection (genetic algorithm); Pattern recognition (psychology); Computer security; Engineering; Artificial neural network","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":[0.001056269,0.0005550891,0.0006216221,0.001072043,0.000264923,0.0006651173,0.000543241,0.0004886881,0.0005031164],"category_scores_gemma":[0.00233627,0.0001441438,0.0005253765,0.0005461981,0.0004315186,0.0006039261,0.0003132806,0.0004108391,0.0001513281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006856988,"about_ca_system_score_gemma":0.0007942443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003168193,"about_ca_topic_score_gemma":0.0027487,"domain_scores_codex":[0.9993363,0.0002453758,0.00004152882,0.000115583,0.0001809649,0.00008014727],"domain_scores_gemma":[0.9982637,0.0008906394,0.0002409669,0.0001701034,0.0003822209,0.00005246035],"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":[0.0005481197,0.0005648767,0.01400409,0.00008991725,0.0001588413,0.00009604725,0.00008950436,0.4361699,0.008730198,0.00197369,0.001993661,0.5355812],"study_design_scores_gemma":[0.000008642724,0.00006557012,0.001184775,0.000002449248,0.000008843343,0.00001588191,0.00001069802,0.9957212,0.002360077,0.0005139745,0.0001036178,0.000004164142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4575586,0.00037455,0.537169,0.0003319258,0.00005501744,0.0001223964,0.0001410546,0.001632373,0.002615122],"genre_scores_gemma":[0.9344265,0.00005647732,0.06445736,0.00005169919,0.00002695226,0.00005388718,0.0001787983,0.00001986955,0.000728385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003168193,"threshold_uncertainty_score":0.006299496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03748161873626838,"score_gpt":0.189044929438568,"score_spread":0.1515633107022996,"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."}}