{"id":"W3155937655","doi":"","title":"Ensemble Feature Learning-Based Event Classification for Cyber-Physical Security of the Smart Grid","year":2019,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Smart grid; Ensemble learning; Autoencoder; Cyber-physical system; Machine learning; Artificial intelligence; Random forest; Intrusion detection system; Feature extraction; Data mining; Deep learning; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0008836389,0.0004600228,0.0007064662,0.0005332581,0.0002822754,0.0005162241,0.0005794686,0.0004533663,0.0006323707],"category_scores_gemma":[0.001901823,0.0001897929,0.0005634594,0.0004693681,0.0002101454,0.0009802507,0.0004994386,0.001070509,0.0002685171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004360094,"about_ca_system_score_gemma":0.000398137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002416944,"about_ca_topic_score_gemma":0.002443758,"domain_scores_codex":[0.9995928,0.00007340567,0.00003330782,0.00009015456,0.0001572343,0.00005300449],"domain_scores_gemma":[0.9993778,0.0002043067,0.00007666061,0.00009302387,0.0002235122,0.00002474041],"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":[0.0002776939,0.000203915,0.006357905,0.00005478525,0.000105333,0.0001383891,0.00008061733,0.4850217,0.0237269,0.00285639,0.002177791,0.4789988],"study_design_scores_gemma":[0.000001803247,0.00002636186,0.000638876,0.000002137277,0.00000721109,0.0000142465,0.000004892417,0.9950618,0.003427073,0.0005192638,0.0002929947,0.000003349281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06975439,0.0002578684,0.9269809,0.0001829346,0.00006359651,0.00005109733,0.0001009544,0.001572813,0.001035473],"genre_scores_gemma":[0.8887739,0.0001684661,0.1094022,0.00007975278,0.00003530345,0.00005067646,0.0002677373,0.00004043933,0.001181541],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002416944,"threshold_uncertainty_score":0.004805803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01434641435707553,"score_gpt":0.2567341144965187,"score_spread":0.2423877001394431,"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."}}