{"id":"W4379390239","doi":"10.32920/23296334","title":"A Secure VANET Model for Eavesdropping Attack Prevention","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Ontario Stroke Network","funders":"","keywords":"Eavesdropping; Computer science; Computer security; Encryption; Key (lock); Vulnerability (computing); Computer network; Vehicular ad hoc network; Cryptography; Confidentiality; Wireless ad hoc network; Wireless; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008139256,0.001170474,0.00086697,0.0009283464,0.0007908895,0.001729097,0.002270542,0.00174802,0.00670574],"category_scores_gemma":[0.001936435,0.0004604119,0.00104815,0.0009715877,0.001039834,0.002741594,0.001717582,0.001838068,0.001033689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00144441,"about_ca_system_score_gemma":0.001250883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01178564,"about_ca_topic_score_gemma":0.007939612,"domain_scores_codex":[0.9991863,0.0002552132,0.00004233557,0.0001512461,0.0001813632,0.0001835406],"domain_scores_gemma":[0.9991633,0.0003368362,0.0001162196,0.00007999504,0.0002480437,0.00005571808],"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":[0.00007627362,0.00002610919,0.0003143586,0.00004688589,0.00002691041,0.0002244444,0.00005465944,0.8800543,0.0007997925,0.1114981,0.001576185,0.005301833],"study_design_scores_gemma":[0.000007654196,0.00001935333,0.00003551132,0.000005515314,0.000006326626,0.00002823904,0.00001421409,0.9886907,0.0001056317,0.01013529,0.0009447418,0.000006831372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0359088,0.0009828078,0.9313141,0.001448714,0.0003208396,0.0001866989,0.0008005992,0.0003726634,0.02866471],"genre_scores_gemma":[0.9176889,0.00150894,0.03955205,0.000253527,0.0001473782,0.0003265284,0.000559828,0.0001056509,0.03985723],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01178564,"threshold_uncertainty_score":0.0234341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08376200318345726,"score_gpt":0.3023590027818912,"score_spread":0.2185969995984339,"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."}}