{"id":"W3202621733","doi":"10.18280/ria.350410","title":"Identification of Network Traffic over IOT Platforms","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Traffic classification; Internet of Things; Computer network; Throughput; AdaBoost; Identification (biology); Traffic generation model; Internet traffic; Quality of service; Traffic congestion; Floating car data; The Internet; Artificial intelligence; Computer security; Wireless; Support vector machine; Telecommunications; World Wide Web; Engineering","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":[0.0003209429,0.0004015683,0.0003291621,0.002264009,0.0004856041,0.0007773067,0.0003530239,0.00043666,0.000753728],"category_scores_gemma":[0.001837311,0.0001260391,0.0002083878,0.001167148,0.0001808057,0.0008766393,0.00037106,0.0003781691,0.0003541915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005018868,"about_ca_system_score_gemma":0.0002891942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001631888,"about_ca_topic_score_gemma":0.001384309,"domain_scores_codex":[0.999523,0.00007139045,0.00002679778,0.0000735954,0.0002275731,0.00007761357],"domain_scores_gemma":[0.999418,0.0001476261,0.00009361241,0.0000482957,0.000253382,0.00003905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001026569,0.0006803002,0.140936,0.0002811416,0.0001032265,0.001462418,0.0004891732,0.1426853,0.1264612,0.009216588,0.005481837,0.5711762],"study_design_scores_gemma":[0.000006125126,0.00008384507,0.02801766,0.00002310496,0.00001536355,0.0004768707,0.0002457753,0.9432802,0.02220141,0.003486283,0.00214363,0.00001972902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8618786,0.0002733626,0.1261095,0.0002696332,0.0001145453,0.0001454099,0.0005274741,0.001082933,0.009598536],"genre_scores_gemma":[0.9841187,0.0001092397,0.01383198,0.00002472171,0.00001895798,0.00003360124,0.0004686579,0.00002572726,0.001368387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002264009,"threshold_uncertainty_score":0.003641427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02391140392375735,"score_gpt":0.2584084830698093,"score_spread":0.2344970791460519,"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."}}