{"id":"W4205369834","doi":"10.1007/978-3-030-89698-0_119","title":"Cluster-TRnet: Jointed Model for Real-Time Traffic Identification with High Accuracy","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes on data engineering and communications technologies","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Identification (biology); Convolutional neural network; Network packet; Traffic generation model; Data mining; Set (abstract data type); Traffic classification; Traffic shaping; Data set; Artificial intelligence; Filter (signal processing); Real-time computing; Machine learning; Computer network; Computer vision; Network traffic control","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":[0.0009628059,0.0008586718,0.001061224,0.0004037388,0.0003962716,0.0008710012,0.002164843,0.001101163,0.00332636],"category_scores_gemma":[0.002369603,0.0004517384,0.000606007,0.0007121446,0.0004386534,0.0014081,0.00108358,0.001407003,0.001608105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007858434,"about_ca_system_score_gemma":0.001192981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01193549,"about_ca_topic_score_gemma":0.009693025,"domain_scores_codex":[0.9995081,0.0001333151,0.00002209706,0.0001411515,0.0001399697,0.00005534561],"domain_scores_gemma":[0.9993733,0.0002301944,0.00004528333,0.0001414365,0.000185035,0.00002482659],"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":[0.0001550039,0.00002991972,0.0002654564,0.00003448534,0.0000356069,0.00002349535,0.00001639636,0.9402165,0.001694471,0.005023397,0.002675207,0.04983002],"study_design_scores_gemma":[0.000001811376,0.000005494553,0.00002398318,9.274163e-7,0.00000221964,0.000004136568,8.393495e-7,0.9983993,0.0002764609,0.0009962985,0.0002860624,0.000002412236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00507772,0.0001267264,0.9919444,0.00006455818,0.00005507665,0.00002045217,0.0001694062,0.001822038,0.0007195934],"genre_scores_gemma":[0.5444573,0.0003487097,0.4420185,0.0001482664,0.0001058154,0.0002847636,0.001347522,0.0007742058,0.0105149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01193549,"threshold_uncertainty_score":0.02373207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02788611027355525,"score_gpt":0.2450338190486607,"score_spread":0.2171477087751055,"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."}}