{"id":"W4402401309","doi":"10.1109/tnsm.2024.3457579","title":"Time-Distributed Feature Learning for Internet of Things Network Traffic Classification","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"NovAtel (Canada); Mitel (Canada); Trusted Positioning (Canada); Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Feature (linguistics); The Internet; Computer network; Traffic classification; Internet of Things; Artificial intelligence; Distributed computing; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000643177,0.0008255135,0.0007254791,0.0008170899,0.0003829585,0.0005138952,0.0008829625,0.0007299912,0.000815389],"category_scores_gemma":[0.002562645,0.0002115484,0.0006388044,0.001235038,0.0003304705,0.001289993,0.0006578157,0.001070536,0.0003006677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008803919,"about_ca_system_score_gemma":0.0007716373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003766426,"about_ca_topic_score_gemma":0.003239674,"domain_scores_codex":[0.9996651,0.00004708251,0.00002063944,0.0001017717,0.000114471,0.00005082412],"domain_scores_gemma":[0.9994068,0.0002048792,0.00008611357,0.0000887791,0.0001830718,0.00003030748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000250435,0.0002750402,0.004481296,0.00007917169,0.00008242993,0.000152242,0.00005770457,0.464998,0.01024666,0.006783138,0.005591509,0.5070024],"study_design_scores_gemma":[0.000003322025,0.0000107673,0.0003146034,0.000002174668,0.00000486536,0.00001851949,0.000004660862,0.9961089,0.001096265,0.002144066,0.0002884954,0.000003239737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0488735,0.0003332115,0.9479075,0.000239035,0.00007303808,0.00004152882,0.0002085119,0.001376297,0.0009474866],"genre_scores_gemma":[0.820934,0.0002991723,0.1749405,0.0002071881,0.0001051339,0.0001397329,0.001255371,0.0001163404,0.002002543],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003766426,"threshold_uncertainty_score":0.007488966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01121590844193538,"score_gpt":0.223492087211217,"score_spread":0.2122761787692816,"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."}}