{"id":"W4319791589","doi":"10.1002/nem.2222","title":"A federated semi‐supervised learning approach for network traffic classification","year":2023,"lang":"en","type":"article","venue":"International Journal of Network Management","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Guangzhou Research Collaborative Innovation Projects; Guangzhou University","keywords":"Computer science; Traffic classification; Field (mathematics); Machine learning; Artificial intelligence; Domain (mathematical analysis); Data mining; Supervised learning; Service (business); Deep learning; Semi-supervised learning; Artificial neural network; Computer network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002750413,0.0007523248,0.001553598,0.001333026,0.0007813296,0.001051704,0.002248363,0.00132801,0.001072258],"category_scores_gemma":[0.004150824,0.0003794497,0.0009234326,0.001082947,0.0008954271,0.001668318,0.001427446,0.00137202,0.0004090257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0010041,"about_ca_system_score_gemma":0.001667804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003319675,"about_ca_topic_score_gemma":0.003023243,"domain_scores_codex":[0.9974757,0.0008389463,0.0001715535,0.000676014,0.0005913212,0.0002464494],"domain_scores_gemma":[0.9965116,0.000934397,0.0003073734,0.0007604582,0.001332194,0.000154011],"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":[0.0004251007,0.0007756584,0.004867995,0.00007306116,0.000166007,0.0001422017,0.0001714981,0.5911328,0.005278241,0.006841966,0.004141114,0.3859843],"study_design_scores_gemma":[0.000003618125,0.00001330732,0.0001167874,0.000001389025,0.000003168314,0.000008377726,0.000006252822,0.9974244,0.0004825807,0.001834422,0.000102927,0.00000284519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05295489,0.000124762,0.9439254,0.0002072304,0.00004268846,0.00007326522,0.0001033235,0.001587396,0.00098092],"genre_scores_gemma":[0.8489447,0.00006169862,0.147825,0.0001697535,0.00007213822,0.0001707067,0.0005484018,0.00006536255,0.00214217],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003319675,"threshold_uncertainty_score":0.01454574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0293230076973299,"score_gpt":0.267264227003791,"score_spread":0.2379412193064611,"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."}}