{"id":"W2973686901","doi":"10.1109/icmcis.2019.8842671","title":"Machine Learning-Based Traffic Classification of Wireless Traffic","year":2019,"lang":"en","type":"article","venue":"","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Wireless; Computer network; Traffic classification; Artificial intelligence; Machine learning; Telecommunications; Quality of service","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003875152,0.0001436676,0.0002647542,0.0001578373,0.000047497,0.00006475896,0.0006596323,0.00007645367,0.0002187878],"category_scores_gemma":[0.00001472192,0.000118183,0.0001725281,0.0004135406,0.00003143941,0.0001625539,0.00004126159,0.0001961887,0.0001804931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002773217,"about_ca_system_score_gemma":0.00005818719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005714583,"about_ca_topic_score_gemma":0.0000476665,"domain_scores_codex":[0.9985886,0.0001004252,0.0003766184,0.0003855085,0.0003278629,0.0002209354],"domain_scores_gemma":[0.99925,0.0001077436,0.0001979504,0.0002741698,0.0001129994,0.00005715272],"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":[0.000009267014,0.0001488086,0.0002988495,0.00003188466,0.0000320413,0.000001574021,0.0004852244,0.8718658,0.0004812285,0.0906487,0.0000979272,0.03589868],"study_design_scores_gemma":[0.0003247891,0.0001245088,0.0004506131,0.00001975747,0.000009741438,0.000001629154,0.00006424241,0.9977723,0.0002715668,7.690097e-7,0.000816995,0.0001430697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.73355,0.00004595186,0.2646125,0.0002451422,0.000133828,0.00009958826,5.138913e-7,0.0001859278,0.001126608],"genre_scores_gemma":[0.9969906,0.000002845603,0.001636939,0.00008128129,0.00002160902,0.000003150559,0.00001289265,0.00001058652,0.001240139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2634406,"threshold_uncertainty_score":0.4819362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01298785061015325,"score_gpt":0.2281792878746504,"score_spread":0.2151914372644972,"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."}}