{"id":"W4290988474","doi":"10.1016/j.vehcom.2022.100517","title":"A coalitional security game against data integrity attacks in autonomous vehicle networks","year":2022,"lang":"en","type":"article","venue":"Vehicular Communications","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Exploit; Spoofing attack; Computer security; Flexibility (engineering); Intelligent transportation system; Reputation; Data aggregator; Wireless sensor network; Computer network; Transport engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001817511,0.0007680646,0.0007886325,0.0005662185,0.001041705,0.001865621,0.001743314,0.001783862,0.003419475],"category_scores_gemma":[0.007913163,0.0003155094,0.0004811177,0.0004387928,0.002155497,0.002488949,0.002824561,0.001726915,0.0002727047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001403159,"about_ca_system_score_gemma":0.001821501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003498041,"about_ca_topic_score_gemma":0.002658445,"domain_scores_codex":[0.9986635,0.0005986383,0.0000455167,0.0001478675,0.0002863562,0.00025821],"domain_scores_gemma":[0.9948587,0.003496366,0.0003334777,0.0003080554,0.0003922124,0.0006111276],"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":[0.001048584,0.0002452886,0.001280946,0.0001136056,0.0001358017,0.0003346565,0.000421683,0.5878424,0.005691909,0.364489,0.005757933,0.03263823],"study_design_scores_gemma":[0.0000604673,0.0001412022,0.0001177654,0.00001305544,0.00001439305,0.00005048543,0.00009384071,0.9004458,0.0005273129,0.09720572,0.001317972,0.00001195651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3615165,0.0002984554,0.5734454,0.00275188,0.0002448928,0.0004677243,0.0001922325,0.0003956603,0.06068718],"genre_scores_gemma":[0.9827908,0.00007339886,0.01187136,0.000115717,0.00002300694,0.00007880604,0.00004075433,0.00001921463,0.004986912],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003498041,"threshold_uncertainty_score":0.01143932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03919603001782763,"score_gpt":0.2700933230446815,"score_spread":0.2308972930268539,"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."}}