{"id":"W4317659178","doi":"10.1145/3566132","title":"DT-DS: CAN Intrusion Detection with Decision Tree Ensembles","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Cyber-Physical Systems","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"AdaBoost; Computer science; Decision tree; Boosting (machine learning); Random forest; Intrusion detection system; Ensemble learning; Machine learning; Artificial intelligence; Gradient boosting; F1 score; Tree (set theory); Data mining; Support vector machine; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001562996,0.0003545767,0.0003961925,0.0002586744,0.0002771176,0.0001056124,0.0002996223,0.0001516457,0.00001128849],"category_scores_gemma":[0.00001756855,0.000300028,0.0001605192,0.001199408,0.00004496803,0.0001818326,0.000009123394,0.0004725034,0.000458268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002637563,"about_ca_system_score_gemma":0.00002461887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002541098,"about_ca_topic_score_gemma":0.002226934,"domain_scores_codex":[0.9980841,0.00007939154,0.0003158769,0.0004271023,0.0005502619,0.0005433079],"domain_scores_gemma":[0.9984883,0.0003998129,0.00004608401,0.0007900112,0.00006199403,0.0002138199],"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":[0.00004861977,0.00005490893,0.00001211355,0.00004437289,0.00009381508,0.00003012367,0.0001868324,0.862173,0.0133376,0.00002549915,0.0002366423,0.1237565],"study_design_scores_gemma":[0.001025208,0.0004305842,0.001365236,0.000458277,0.0001319047,0.0001190751,0.0003462184,0.9687409,0.02236772,0.0004467321,0.003878339,0.0006897993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8147946,0.00005177435,0.1814682,0.00007894174,0.001048942,0.0005136161,0.0000257362,0.001634723,0.00038348],"genre_scores_gemma":[0.9987398,0.00006369998,0.0002491033,0.00001338977,0.000377323,0.0002111676,0.0000220283,0.0001186949,0.0002047464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1839453,"threshold_uncertainty_score":0.9999452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00981177346925443,"score_gpt":0.2134110603622379,"score_spread":0.2035992868929835,"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."}}