{"id":"W2979687053","doi":"10.1109/ccece.2019.8861934","title":"Encrypted Traffic Classification Based ML for Identifying Different Social Media Applications","year":2019,"lang":"en","type":"article","venue":"","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Encryption; Computer science; Social media; Identification (biology); Context (archaeology); Big data; Entertainment; Software deployment; Traffic classification; Computer security; Data science; Artificial intelligence; Machine learning; World Wide Web; Data mining; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001096181,0.00077755,0.0006729192,0.00364268,0.000496447,0.001336816,0.0009102903,0.0008239402,0.001506878],"category_scores_gemma":[0.003925567,0.000178654,0.0005959819,0.001539549,0.0003814341,0.00143807,0.0008875461,0.0008303666,0.001456766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006350088,"about_ca_system_score_gemma":0.0005363002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001608123,"about_ca_topic_score_gemma":0.001515404,"domain_scores_codex":[0.9988596,0.000338697,0.00009862099,0.000249735,0.0002976807,0.0001556686],"domain_scores_gemma":[0.998111,0.0007515495,0.0002619429,0.0002472231,0.0005363873,0.00009194572],"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":[0.0005787822,0.00114669,0.04400582,0.0001523752,0.0001836951,0.0003609415,0.0003340145,0.1394096,0.02545238,0.01149394,0.005104351,0.7717774],"study_design_scores_gemma":[0.000003707783,0.00003184757,0.001823322,0.000004989402,0.000009869344,0.00005944149,0.00004931671,0.9911192,0.004064117,0.002183246,0.000643541,0.000007469649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2179261,0.0003025093,0.7731301,0.00052907,0.0001444595,0.000224983,0.0006326247,0.002612056,0.004498184],"genre_scores_gemma":[0.8903815,0.0001618047,0.1043064,0.0001072195,0.0001412846,0.0001372895,0.00118299,0.00006241941,0.003519121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00364268,"threshold_uncertainty_score":0.005797207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04256416877298179,"score_gpt":0.279826302258437,"score_spread":0.2372621334854552,"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."}}