{"id":"W4391991244","doi":"10.32920/25262815","title":"Efficient Traffic Classification Using Hybrid Deep Learning","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Traffic classification; Machine learning; World Wide Web; The Internet","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007482774,0.0003865281,0.0004237905,0.000399791,0.00017493,0.001024869,0.001231475,0.0001968142,0.0000668615],"category_scores_gemma":[0.00004437042,0.0003417323,0.0004464869,0.0003572066,0.00004346064,0.00004017899,0.00189157,0.001494366,0.000308902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002557673,"about_ca_system_score_gemma":0.0001583545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001598666,"about_ca_topic_score_gemma":0.00001489747,"domain_scores_codex":[0.9969547,0.000162076,0.0006365536,0.001254848,0.0005546475,0.000437137],"domain_scores_gemma":[0.9988945,0.00007706218,0.0002701721,0.0004798486,0.000165414,0.0001130422],"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":[9.541347e-7,0.00003615688,0.000001600037,0.00007441403,0.0000801625,0.00002719472,0.0008463967,0.8836815,0.00004422568,0.09076621,0.00009926705,0.0243419],"study_design_scores_gemma":[0.00005211106,0.0000159863,0.00001155564,0.000177193,0.0000824356,0.00002710854,0.0001149353,0.9986846,0.00004350673,0.00008567946,0.000317816,0.0003871155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2543171,0.0006260326,0.7401298,0.0002043278,0.001339502,0.0001541958,0.000001019098,0.0007141372,0.002513865],"genre_scores_gemma":[0.9799404,0.00001046949,0.01892072,0.00006175582,0.0002884244,0.00001162172,0.0000214438,0.00003461865,0.0007105555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7256233,"threshold_uncertainty_score":0.9999034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02873595951645934,"score_gpt":0.2691591244423485,"score_spread":0.2404231649258891,"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."}}