{"id":"W3041737507","doi":"10.1109/jiot.2020.3008488","title":"<i>Ad Hoc</i> Vehicular Fog Enabling Cooperative Low-Latency Intrusion Detection","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"Khalifa University of Science, Technology and Research; Lebanese American University","keywords":"Computer science; Cloud computing; Intrusion detection system; Vehicular ad hoc network; Computer network; Computation offloading; Wireless ad hoc network; Distributed computing; Latency (audio); Server; Edge computing; Survivability; Wireless; Computer security","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":[0.0001494282,0.0003419091,0.0002549488,0.0002072978,0.0003638613,0.0006058124,0.000697309,0.0003612088,0.0004621205],"category_scores_gemma":[0.0002064187,0.0000991897,0.0002755394,0.0002875902,0.0003557972,0.0004238934,0.0005889314,0.0002909459,0.0001335402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003883607,"about_ca_system_score_gemma":0.0005380648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003198081,"about_ca_topic_score_gemma":0.005731361,"domain_scores_codex":[0.9998686,0.00001852243,0.000005418052,0.0000250182,0.00003365376,0.00004884359],"domain_scores_gemma":[0.9999102,0.00001624364,0.00001482494,0.00001759774,0.00002637679,0.00001475059],"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":[0.0002967087,0.0002144338,0.005622161,0.0003203055,0.0001324198,0.001090637,0.0003115818,0.5441892,0.1528238,0.06049139,0.009103967,0.2254034],"study_design_scores_gemma":[0.000007440934,0.0001560781,0.0009780705,0.00001642751,0.00003269871,0.0002562781,0.0001201866,0.9559205,0.02483292,0.007143867,0.0105197,0.00001584835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1236478,0.0008534963,0.85368,0.000278301,0.0001918339,0.0001012583,0.00009607687,0.0007373065,0.02041387],"genre_scores_gemma":[0.9576445,0.0003111014,0.03924903,0.0000853301,0.00002081699,0.00002155015,0.00008198552,0.00001767141,0.002568059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003198081,"threshold_uncertainty_score":0.006358922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009260810637477883,"score_gpt":0.2003083942408793,"score_spread":0.1910475836034014,"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."}}