{"id":"W3206867092","doi":"10.1109/isc253183.2021.9562830","title":"NeuroCAN: Contextual Anomaly Detection in Controller Area Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Computer science; Anomaly detection; Focus (optics); Controller (irrigation); Real-time computing; CAN bus; Transmission (telecommunications); Artificial intelligence; Computer hardware; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005774125,0.001342683,0.0009412021,0.001369194,0.0004291038,0.0007002004,0.00189815,0.0008293043,0.0008203955],"category_scores_gemma":[0.002653559,0.0003354443,0.0005356067,0.001075346,0.0004896505,0.001259215,0.001098621,0.001177792,0.0003085698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001121133,"about_ca_system_score_gemma":0.001352146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01753963,"about_ca_topic_score_gemma":0.02275827,"domain_scores_codex":[0.9994084,0.00007720471,0.00003522796,0.0002279138,0.0001633101,0.00008786467],"domain_scores_gemma":[0.9992865,0.0002389346,0.0001242217,0.00009331407,0.0002116325,0.00004534867],"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":[0.0004535034,0.000332047,0.01647935,0.0002285819,0.0002772379,0.0003556515,0.00009681222,0.5567699,0.008156971,0.003661428,0.01802305,0.3951656],"study_design_scores_gemma":[0.000008815448,0.0000409998,0.001068165,0.000007627053,0.00001737303,0.00005109764,0.00002260112,0.9922339,0.002831782,0.002434517,0.001273467,0.000009695014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2095656,0.004152468,0.7525362,0.001049221,0.0004912665,0.0002806512,0.004752416,0.0223442,0.004827893],"genre_scores_gemma":[0.8709061,0.0007485926,0.1190606,0.0003999931,0.0001233659,0.0001849632,0.005447198,0.0001709836,0.00295812],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01753963,"threshold_uncertainty_score":0.03487504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006391722515535278,"score_gpt":0.1776804259719379,"score_spread":0.1712887034564027,"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."}}