{"id":"W4386223270","doi":"10.3390/s23177464","title":"Ensemble Model Based on Hybrid Deep Learning for Intrusion Detection in Smart Grid Networks","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"King Abdulaziz University","keywords":"Computer science; Smart grid; Intrusion detection system; Reliability (semiconductor); Grid; Anomaly detection; Denial-of-service attack; Deep learning; SCADA; Resilience (materials science); Real-time computing; Distributed computing; Artificial intelligence; Computer security; Engineering; The Internet; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008281056,0.0007088137,0.0009906632,0.0005699777,0.0002450501,0.0005503042,0.00103823,0.000581933,0.0007888903],"category_scores_gemma":[0.001561721,0.0003356046,0.0005981295,0.0005878895,0.000237042,0.00104457,0.0006638102,0.001044974,0.0002074054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007025824,"about_ca_system_score_gemma":0.0005884551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01174711,"about_ca_topic_score_gemma":0.01236273,"domain_scores_codex":[0.9997118,0.00007108433,0.00001844173,0.00007509104,0.00006835409,0.0000550946],"domain_scores_gemma":[0.9995284,0.000216845,0.00004404373,0.00004850729,0.0001404708,0.00002166287],"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":[0.00007730705,0.00006148491,0.001465955,0.00001736436,0.00005679937,0.00003010424,0.00001615147,0.9357761,0.001027338,0.00104252,0.0008547189,0.05957403],"study_design_scores_gemma":[5.273885e-7,0.000004188349,0.00004742798,4.625051e-7,0.000001902952,0.00000129133,6.0923e-7,0.9996622,0.0001069535,0.0001479426,0.00002591051,7.416688e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1730843,0.001058283,0.8198447,0.0004011768,0.0001198315,0.00004369465,0.00029729,0.00272947,0.002421177],"genre_scores_gemma":[0.9518418,0.0002635156,0.04524997,0.0001285812,0.00003429817,0.00005892571,0.0005036058,0.00005341634,0.001865921],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01174711,"threshold_uncertainty_score":0.02335751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008659955713557698,"score_gpt":0.2089822610251784,"score_spread":0.2003223053116207,"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."}}