{"id":"W4296887680","doi":"10.18280/isi.270410","title":"Sybil Attack Detection in VANET Using Machine Learning Approach","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sybil attack; Computer science; Vehicular ad hoc network; Node (physics); Computer security; Denial-of-service attack; Scalability; Wireless ad hoc network; Computer network; Wireless sensor network; Telecommunications; Engineering; World Wide Web; Wireless; Database","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":[0.001120803,0.0006570447,0.000874789,0.001565259,0.0006085946,0.001272437,0.0009868592,0.001032589,0.0006786369],"category_scores_gemma":[0.003033162,0.000292855,0.0007481189,0.0006740126,0.0006011769,0.001581331,0.0009118828,0.001084169,0.0002055628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007277618,"about_ca_system_score_gemma":0.0006296911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002376147,"about_ca_topic_score_gemma":0.001510762,"domain_scores_codex":[0.99879,0.0004045176,0.00007378928,0.0002282185,0.0003145673,0.0001889263],"domain_scores_gemma":[0.9987208,0.0006281354,0.000169598,0.00008708156,0.0003320182,0.00006221989],"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":[0.000237997,0.0002379632,0.009086902,0.0001272754,0.0002032678,0.0003680193,0.0001629383,0.7775717,0.006718738,0.01641377,0.002930472,0.185941],"study_design_scores_gemma":[0.000002071144,0.00002049948,0.0002147142,0.000002289796,0.000004333976,0.00003754667,0.00001457825,0.9973167,0.0005402096,0.00165847,0.0001832913,0.000005352287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06824851,0.0004720223,0.9265617,0.0004593428,0.0001174245,0.0001179176,0.00005596683,0.0007914244,0.00317567],"genre_scores_gemma":[0.9294418,0.0002796726,0.06771433,0.0001244494,0.00006896492,0.00008561568,0.0001322871,0.00002186255,0.002131059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002376147,"threshold_uncertainty_score":0.005927444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02106561409627886,"score_gpt":0.2283816221280339,"score_spread":0.2073160080317551,"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."}}