{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002258286,0.000128688,0.0001885462,0.0001217343,0.0001616748,0.0001991705,0.0006111437,0.00007654376,0.0001036196],"category_scores_gemma":[0.0000182801,0.0001080047,0.0001869434,0.0002562022,0.00001935104,0.0001695519,0.0000433425,0.00009100818,0.0001220281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005540492,"about_ca_system_score_gemma":0.00003574065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.433971e-7,"about_ca_topic_score_gemma":0.00004295723,"domain_scores_codex":[0.9987387,0.00003887282,0.0003074221,0.0004221977,0.0002575875,0.0002352034],"domain_scores_gemma":[0.9992129,0.0002640244,0.00013107,0.0002107629,0.0001285683,0.00005271133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005133295,0.0001301629,0.00003990371,0.0000362248,0.0000366466,1.687516e-7,0.001522231,0.001977143,0.001565799,0.954004,0.0008787925,0.03980372],"study_design_scores_gemma":[0.0003190703,0.00001319598,0.001181438,0.000006817185,0.0000179765,2.267184e-7,0.0001756433,0.9965507,0.0002597279,0.00008957666,0.001241372,0.0001442884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08390196,0.00001206829,0.9143202,0.0004941198,0.0002054368,0.0003645047,0.000002504522,0.0002038337,0.0004953377],"genre_scores_gemma":[0.9892132,7.499336e-7,0.01005623,0.0001526348,0.0001493693,0.0001434897,0.00005358882,0.00001002142,0.0002207077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9945735,"threshold_uncertainty_score":0.4404305,"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."}}