{"id":"W2949160428","doi":"10.1109/tii.2019.2922215","title":"On Feasibility and Limitations of Detecting False Data Injection Attacks on Power Grid State Estimation Using D-FACTS Devices","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":190,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Science Foundation of Zhejiang Province; National Key Research and Development Program of China; National Research Foundation Singapore; National Natural Science Foundation of China; Nanyang Technological University; Ministry of Education - Singapore","keywords":"Software deployment; Grid; Computer science; Power grid; State (computer science); Power (physics); Tree (set theory); Real-time computing; Distributed computing; Data mining; Reliability engineering; Engineering; Algorithm; Mathematics; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003217382,0.0001720267,0.0001992335,0.000246239,0.0001599622,0.00006633434,0.0001495822,0.0001765319,0.00001170175],"category_scores_gemma":[0.0001046664,0.0001671712,0.00003547564,0.0003173923,0.00004672051,0.000879907,0.00000360497,0.0005253567,0.00002465662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001200561,"about_ca_system_score_gemma":0.00005060924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003510945,"about_ca_topic_score_gemma":0.0000800999,"domain_scores_codex":[0.9988362,0.00004014027,0.0005412285,0.00013162,0.0002685801,0.00018221],"domain_scores_gemma":[0.9986777,0.0006301426,0.0001454739,0.0004202349,0.0000568875,0.0000695461],"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":[0.00008509132,0.00005492832,0.0000863771,0.00007083987,0.00003405321,1.881793e-7,0.001447317,0.9857243,0.0002016326,0.000009458435,0.00003077243,0.01225499],"study_design_scores_gemma":[0.001108071,0.0005783857,0.0003949124,0.0003204718,0.00005205314,0.000009864435,0.001303898,0.9732111,0.02259107,0.00005195936,0.0001082541,0.0002699829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9038711,0.000005722077,0.09348996,0.000008811956,0.001700039,0.0004284272,0.0001516811,0.00009452951,0.0002497355],"genre_scores_gemma":[0.9991151,0.00003013349,0.0007615559,0.00002791081,0.00002518377,0.000004020399,0.00001629071,0.00001457988,0.000005212808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09524401,"threshold_uncertainty_score":0.6817043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1283102173708932,"score_gpt":0.2930409280313461,"score_spread":0.1647307106604529,"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."}}