{"id":"W3135872604","doi":"10.1109/tcns.2020.3024315","title":"A Blended Active Detection Strategy for False Data Injection Attacks in Cyber-Physical Systems","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Control of Network Systems","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Digital watermarking; Computer science; Covert; Scheme (mathematics); Detector; Limit (mathematics); Computer security; Function (biology); Cyber-physical system; Real-time computing; Artificial intelligence; Image (mathematics); Telecommunications","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":[0.001244695,0.0008595343,0.0008531034,0.0010429,0.0004356277,0.001248425,0.001376837,0.001476124,0.001079779],"category_scores_gemma":[0.003220864,0.0003144116,0.000571178,0.0004065611,0.001401617,0.0029547,0.001984815,0.001391566,0.0003611197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004397189,"about_ca_system_score_gemma":0.0005180173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001972657,"about_ca_topic_score_gemma":0.000222992,"domain_scores_codex":[0.998556,0.0003459505,0.00008967007,0.0002708254,0.0006146373,0.0001230977],"domain_scores_gemma":[0.9979201,0.0007823137,0.0003702104,0.0004275235,0.0003773999,0.0001225212],"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":[0.0009847454,0.0004113628,0.002419213,0.0003915889,0.0001879674,0.0006998738,0.0006369932,0.1275029,0.2372532,0.1108585,0.00164251,0.5170111],"study_design_scores_gemma":[0.00003923087,0.0007004217,0.0004941766,0.00003368017,0.00004705195,0.0006798481,0.00005411093,0.9253394,0.05999159,0.009979973,0.002594209,0.00004626658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02709442,0.0003602697,0.9705957,0.0001329535,0.00004460106,0.00004745803,0.000009230655,0.000310119,0.001405263],"genre_scores_gemma":[0.8739623,0.000277048,0.1233249,0.0001753334,0.0000532263,0.00005091761,0.00001917498,0.0000239832,0.002113143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001476124,"threshold_uncertainty_score":0.006582677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03255688954800123,"score_gpt":0.2539078176066916,"score_spread":0.2213509280586904,"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."}}