{"id":"W3126726356","doi":"10.1109/lnet.2021.3058292","title":"Securing the Internet of Vehicles: A Deep Learning-Based Classification Framework","year":2021,"lang":"en","type":"article","venue":"IEEE Networking Letters","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; The Internet; Identification (biology); Authentication (law); Deep learning; Computer security; Artificial intelligence; Computer network; Machine learning; World Wide Web","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":[0.0003255284,0.0002294501,0.0002499248,0.00005985102,0.0000893738,0.00008411443,0.0003199075,0.0001577965,0.00002784043],"category_scores_gemma":[0.00003569554,0.0002158705,0.0001567357,0.0004873034,0.00009090452,0.00006680068,0.00003344322,0.0008798905,0.00002452344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000115221,"about_ca_system_score_gemma":0.00001924246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000809435,"about_ca_topic_score_gemma":0.0000245865,"domain_scores_codex":[0.9983954,0.0001775095,0.000372437,0.0002831379,0.0002954976,0.0004760329],"domain_scores_gemma":[0.9988585,0.0004429461,0.000114919,0.0004663467,0.00004834274,0.00006894237],"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":[0.000007883208,0.00001013453,0.002670325,0.00005529933,0.00008653518,0.00003204904,0.0004705844,0.9770206,0.008476362,0.00005740198,0.002383587,0.008729241],"study_design_scores_gemma":[0.0001918833,0.00001213074,0.001846484,0.0003693098,0.00004362327,0.0000123634,0.00005369473,0.9764253,0.006519701,0.00005716443,0.01423552,0.0002328528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6113827,0.00169411,0.3832998,0.0008171391,0.001968018,0.0001574201,6.779416e-7,0.0003808621,0.0002992903],"genre_scores_gemma":[0.9961123,0.00007453941,0.001437236,0.001129594,0.001101543,0.00002805117,0.00001556739,0.00008251349,0.00001858419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3847297,"threshold_uncertainty_score":0.8802943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01169599996625076,"score_gpt":0.2094557228910807,"score_spread":0.19775972292483,"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."}}