{"id":"W4210568765","doi":"10.1145/3508467.3508469","title":"Data analytics for cybersecurity enhancement of transformer protection","year":2021,"lang":"en","type":"article","venue":"ACM SIGEnergy Energy Informatics Review","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; University of Toronto","funders":"","keywords":"Autoencoder; Computer science; IEC 61850; Anomaly detection; Smart grid; Software deployment; Deep learning; Transformer; Convolutional neural network; Context (archaeology); Artificial intelligence; Computer security; Real-time computing; Machine learning; Data mining; Engineering","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":[0.0008951144,0.0005509151,0.0003751168,0.001255521,0.0001515542,0.0009707203,0.0006144409,0.0004478592,0.0009982431],"category_scores_gemma":[0.00312232,0.0001566933,0.0003520256,0.001229724,0.0002718092,0.001923087,0.0005892038,0.0009954097,0.0003683731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006253651,"about_ca_system_score_gemma":0.0006569232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002178436,"about_ca_topic_score_gemma":0.002614124,"domain_scores_codex":[0.9995713,0.00009967895,0.00003058525,0.0000779512,0.0001893025,0.00003110333],"domain_scores_gemma":[0.9986919,0.0004797317,0.0001316301,0.0001636692,0.0004961673,0.00003689025],"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":[0.0002527742,0.0001949674,0.009370602,0.0005212945,0.0001386643,0.0001339897,0.0001611519,0.07572363,0.01931894,0.007874529,0.01117148,0.875138],"study_design_scores_gemma":[0.00001781181,0.000194557,0.009227046,0.000145694,0.00007555144,0.0001856124,0.0001980799,0.920224,0.03239654,0.01860768,0.01870059,0.00002685438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1682248,0.01859648,0.7879375,0.005872846,0.0004852515,0.0002274513,0.001255821,0.005231037,0.01216878],"genre_scores_gemma":[0.9038879,0.007802875,0.08421715,0.0002621322,0.0001139203,0.00006021995,0.00123189,0.00005920354,0.002364754],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002178436,"threshold_uncertainty_score":0.00473386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08245398385557982,"score_gpt":0.3156642491496253,"score_spread":0.2332102652940455,"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."}}