{"id":"W2904326537","doi":"10.1109/ickii.2018.8569113","title":"Tor Traffic Classification from Raw Packet Header using Convolutional Neural Network","year":2018,"lang":"en","type":"article","venue":"2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII)","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Header; Computer science; Traffic classification; Encryption; Deep packet inspection; Convolutional neural network; Traffic generation model; Network packet; Artificial intelligence; Data mining; Machine learning; Computer network","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":[0.0005724852,0.0002871218,0.0002640813,0.0004240435,0.0003529616,0.0004857705,0.0006292384,0.0001824003,0.000773226],"category_scores_gemma":[0.00006773839,0.0002825522,0.0001080769,0.0008176432,0.0002687776,0.0008047453,0.0001069492,0.0002951927,0.0003041431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001895745,"about_ca_system_score_gemma":0.0001717414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001881826,"about_ca_topic_score_gemma":0.00008454488,"domain_scores_codex":[0.9974698,0.0001669778,0.0008353656,0.000702582,0.0004986284,0.0003266392],"domain_scores_gemma":[0.9974163,0.00007827115,0.0004839362,0.00025362,0.001674923,0.00009291997],"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":[0.0000463511,0.0001728043,0.0003093782,0.000006563173,0.0001051915,0.000001342827,0.00057848,0.0009983805,0.002789239,0.9750682,0.01039887,0.009525256],"study_design_scores_gemma":[0.0005869787,0.0001272945,0.002810762,0.0001390553,0.00001679245,0.000008398929,0.0001441111,0.9891349,0.0002790149,0.002314285,0.004135046,0.0003033451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5775912,0.00006117021,0.4055713,0.002021311,0.005642083,0.0002340657,0.00001917215,0.0001967189,0.008663061],"genre_scores_gemma":[0.9936433,0.00001310745,0.002141051,0.0008746185,0.002077211,0.00001677316,0.0002132824,0.00001733533,0.001003353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9881365,"threshold_uncertainty_score":0.9999627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1561300172782004,"score_gpt":0.3424347121123958,"score_spread":0.1863046948341954,"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."}}