{"id":"W4313503802","doi":"10.3390/electronics12020269","title":"ML-Based Traffic Classification in an SDN-Enabled Cloud Environment","year":2023,"lang":"en","type":"article","venue":"Electronics","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Agence Universitaire de la Francophonie","keywords":"Cloud computing; Computer science; Traffic classification; C4.5 algorithm; Naive Bayes classifier; Troubleshooting; Quality of service; Support vector machine; Random forest; Machine learning; Data mining; Artificial intelligence; Computer network; Distributed computing; Operating system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006427487,0.000139268,0.0001648789,0.0002023504,0.00008501677,0.0001142557,0.0006382768,0.00008146017,0.00002792663],"category_scores_gemma":[0.0000136333,0.0001374656,0.00007749061,0.0006596548,0.00002010724,0.0001959995,0.00004596544,0.0002582548,0.0002421426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002426544,"about_ca_system_score_gemma":0.0001164471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001470269,"about_ca_topic_score_gemma":0.0001493128,"domain_scores_codex":[0.9983024,0.0001228196,0.0002886355,0.0004663221,0.0002911175,0.0005286995],"domain_scores_gemma":[0.9994352,0.00005423491,0.00008447247,0.0003390976,0.00001932978,0.0000677007],"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":[0.00001567257,0.0002926973,0.0001835852,0.00001281779,0.00002726658,0.00002913478,0.001461742,0.7225408,0.001561571,0.2165405,0.001077656,0.05625657],"study_design_scores_gemma":[0.0002595467,0.0001346955,0.0007795666,0.000006799977,0.000005944276,9.875375e-7,0.00004802608,0.9894692,0.0002732937,0.00005398459,0.008810453,0.0001574761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8366557,0.0003411814,0.1612404,0.0009734266,0.0001520859,0.0001531906,6.215714e-7,0.0003375131,0.000145895],"genre_scores_gemma":[0.9985145,0.00007345059,0.0009025491,0.0001507035,0.00007381802,0.00002144036,0.00003510025,0.00001392847,0.0002145344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2669284,"threshold_uncertainty_score":0.5605683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01690465271922363,"score_gpt":0.2385600724832246,"score_spread":0.221655419764001,"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."}}