{"id":"W4402713914","doi":"10.1016/j.epsr.2024.111076","title":"A deep learning deviation-based scheme to defend against false data injection attacks in power distribution systems","year":2024,"lang":"en","type":"article","venue":"Electric Power Systems Research","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Iran National Science Foundation","keywords":"Scheme (mathematics); Artificial intelligence; Power (physics); Distribution (mathematics); Deep learning; Computer science; Pattern recognition (psychology); Data mining; Mathematics; Physics","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":[0.001882812,0.0006070547,0.0009168687,0.0004468928,0.0005244099,0.0008680269,0.001591753,0.001033036,0.001430953],"category_scores_gemma":[0.005051721,0.0002525356,0.0003766934,0.0004418557,0.001007739,0.001703998,0.002281039,0.002076017,0.0003287829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009267918,"about_ca_system_score_gemma":0.001345864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001512265,"about_ca_topic_score_gemma":0.00230963,"domain_scores_codex":[0.9987723,0.000288655,0.00007508776,0.0002317361,0.0004072323,0.0002249725],"domain_scores_gemma":[0.9974726,0.001051836,0.0002802345,0.0005303776,0.0005013844,0.0001635333],"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":[0.001219037,0.0002482026,0.002841041,0.0001290726,0.0001340352,0.0001469034,0.000118806,0.6370854,0.01947223,0.03382514,0.005863338,0.2989167],"study_design_scores_gemma":[0.000008887969,0.00004844425,0.0001335608,0.000004239474,0.000006168553,0.00002094233,0.000004416242,0.9939414,0.001978192,0.003611243,0.0002363468,0.000006100397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05992159,0.0003880751,0.9352853,0.0006345834,0.0001950328,0.00006228655,0.00009012168,0.001163381,0.002259702],"genre_scores_gemma":[0.9544224,0.00008652172,0.04299801,0.0002185244,0.00005275738,0.00002946326,0.00009392444,0.00002926036,0.002069183],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001882812,"threshold_uncertainty_score":0.009957373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03544581153690596,"score_gpt":0.3213017831506472,"score_spread":0.2858559716137412,"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."}}