{"id":"W4394828294","doi":"10.1109/tcsi.2024.3383839","title":"Resilient Synchronization for Insecure Markovian Jump Neural Networks to Mitigate Dual Cyber Attacks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Science and Technology Major Project; Australian Research Council; Natural Science Foundation of Shanghai","keywords":"Jump; Synchronization (alternating current); Dual (grammatical number); Computer science; Artificial neural network; Computer security; Computer network; Distributed computing; Artificial intelligence; 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":[0.0005476841,0.0007424739,0.0004346923,0.0003156255,0.0003972705,0.0005987087,0.0008060813,0.0006036159,0.001417711],"category_scores_gemma":[0.00139346,0.0001991414,0.0004272435,0.0001719051,0.0006268561,0.000657575,0.000926173,0.0008508036,0.000151783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000519145,"about_ca_system_score_gemma":0.0005706675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002556938,"about_ca_topic_score_gemma":0.001955867,"domain_scores_codex":[0.9996225,0.00004901493,0.00002423371,0.0001324987,0.0001073482,0.00006446708],"domain_scores_gemma":[0.9996463,0.000114322,0.0001005669,0.00003837288,0.00007776177,0.00002261885],"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":[0.0002292052,0.00004973342,0.0008120942,0.0001787183,0.0000799601,0.0002035911,0.0001523395,0.8797778,0.03019966,0.03387783,0.0007800098,0.05365906],"study_design_scores_gemma":[0.000009457961,0.00006144611,0.0001092389,0.000005775116,0.00001278607,0.0000181099,0.000005792215,0.9951808,0.001823214,0.002437927,0.0003298514,0.000005655352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05187034,0.0004309799,0.9417846,0.0001982565,0.0001159921,0.00004343682,0.00003055963,0.0004918588,0.005033985],"genre_scores_gemma":[0.9883615,0.0001668787,0.009948088,0.0000532821,0.00002534374,0.00003359518,0.0000189956,0.00001430348,0.0013779],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002556938,"threshold_uncertainty_score":0.005084097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01327240933117495,"score_gpt":0.2375301221935326,"score_spread":0.2242577128623577,"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."}}