{"id":"W4224998072","doi":"10.5539/cis.v15n2p78","title":"Anomaly Detection Methodology of In-vehicle Network Based on Graph Pattern Matching","year":2022,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Anomaly detection; Graph; De facto; Matching (statistics); CAN bus; Data mining; Protocol (science); Computer network; Theoretical computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007596261,0.0007816647,0.001060565,0.005438182,0.0005387551,0.0008014815,0.001809861,0.0009856505,0.0007227613],"category_scores_gemma":[0.003540498,0.0002429056,0.0007801332,0.003647683,0.0005750884,0.001678128,0.0009240281,0.00073254,0.0003482256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006990334,"about_ca_system_score_gemma":0.00092309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00494722,"about_ca_topic_score_gemma":0.003868208,"domain_scores_codex":[0.9983999,0.0002530435,0.0001191625,0.0004857739,0.0005667005,0.0001754999],"domain_scores_gemma":[0.9980627,0.0005136598,0.0004216258,0.0002562871,0.0006463915,0.00009930665],"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":[0.0004321291,0.0005440245,0.04784901,0.0003340055,0.0004123552,0.001067415,0.000336919,0.2666217,0.03309155,0.01947146,0.0056686,0.6241708],"study_design_scores_gemma":[0.00001282941,0.00007610326,0.003618521,0.000007455286,0.00003616741,0.0003058727,0.00007173362,0.982197,0.005885751,0.006423997,0.001351214,0.00001341418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1138717,0.0002541066,0.8820065,0.0001596738,0.00005470781,0.0001989965,0.0003992438,0.001698119,0.001357043],"genre_scores_gemma":[0.7744204,0.0002509519,0.2220494,0.000080552,0.00004182584,0.0001621277,0.001356799,0.00009180538,0.001546139],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005438182,"threshold_uncertainty_score":0.009836853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01304683617934697,"score_gpt":0.220418842810837,"score_spread":0.2073720066314901,"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."}}