{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001095296,0.0001697717,0.0002483123,0.00006087785,0.00002984091,0.00005039792,0.00008035907,0.0001349383,0.0002134368],"category_scores_gemma":[0.0000293618,0.0001782302,0.00007476391,0.0003324806,0.0000161405,0.0001110425,0.00002862126,0.0003216286,0.00003853692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007933005,"about_ca_system_score_gemma":0.00001463915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000364991,"about_ca_topic_score_gemma":0.003359657,"domain_scores_codex":[0.9989634,0.00005161451,0.0002553382,0.0002225226,0.0001134963,0.000393702],"domain_scores_gemma":[0.999576,0.00008249707,0.00001676344,0.0001910283,0.00004256775,0.00009109138],"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":[0.00001426639,0.00001391891,0.0006477433,0.0000050826,0.00002836105,0.0001370772,0.00001921537,0.9835377,0.002355154,0.00008474517,0.0008446679,0.01231205],"study_design_scores_gemma":[0.0009818866,0.00002074297,0.005603628,0.000014302,0.000009897548,0.00006248171,0.00004303188,0.9860888,0.00155298,0.0000246848,0.005406387,0.0001911629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7481973,0.002285418,0.2252616,0.0001078905,0.001033223,0.0003131831,0.000002154628,0.0007019714,0.02209731],"genre_scores_gemma":[0.9987466,0.00008329531,0.000109078,0.0003049943,0.0001807194,0.00002515691,0.000008398232,0.00004110468,0.0005006747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2505493,"threshold_uncertainty_score":0.7268018,"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."}}