{"id":"W3213796608","doi":"10.1109/wf-iot51360.2021.9595307","title":"Time-Distributed Feature Learning in Network Traffic Classification for Internet of Things","year":2021,"lang":"en","type":"article","venue":"","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Traffic classification; Feature (linguistics); The Internet; Data mining; Feature learning; Convolutional neural network; Perceptron; Artificial intelligence; Data set; Internet traffic; Feature extraction; Machine learning; Artificial neural network; Computer network; 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":[0.0008973373,0.0007098219,0.0008674047,0.001097201,0.0004519508,0.0005589179,0.0008285827,0.0008769864,0.0007829772],"category_scores_gemma":[0.00270918,0.0001701725,0.0006201728,0.00143475,0.0003249497,0.001462584,0.0005011277,0.001073604,0.0002715127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007214291,"about_ca_system_score_gemma":0.0005845666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004467133,"about_ca_topic_score_gemma":0.002707414,"domain_scores_codex":[0.9995397,0.00009440797,0.00003620841,0.0001440699,0.0001014188,0.00008420499],"domain_scores_gemma":[0.998964,0.0004806243,0.0001039383,0.0001287656,0.000268256,0.0000544243],"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":[0.0004543312,0.0008245207,0.009776417,0.00008275264,0.0001030414,0.0001890073,0.00008503722,0.3480977,0.008443047,0.003672601,0.005120554,0.623151],"study_design_scores_gemma":[0.000004383845,0.00002134332,0.0007072277,0.000002064464,0.000005568485,0.00002002105,0.00001225446,0.996458,0.001059475,0.001505495,0.0002000407,0.000004108238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3048847,0.0009270841,0.6888573,0.0006733141,0.0001909303,0.00008899142,0.0004556817,0.002238583,0.00168344],"genre_scores_gemma":[0.943727,0.0001866276,0.05394023,0.00009617154,0.00008847831,0.00007032545,0.0008067171,0.00004436652,0.001040179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004467133,"threshold_uncertainty_score":0.008882284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0130288077356614,"score_gpt":0.2334968963210757,"score_spread":0.2204680885854143,"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."}}