{"id":"W3167826121","doi":"10.1109/syscon48628.2021.9447099","title":"Network Traffic Flow Based Machine Learning Technique for IoT Device Identification","year":2021,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Internet of Things; Identification (biology); Scheme (mathematics); Flow network; Data mining; Artificial intelligence; Computer network; Machine learning; Real-time computing; Embedded system","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":[0.0006008272,0.00009802645,0.0001111828,0.00004990799,0.0003636284,0.0001807603,0.000279846,0.00008690172,0.00008089118],"category_scores_gemma":[0.00007750464,0.00009886896,0.00007636192,0.0006506671,0.00001165309,0.000171497,0.00006970063,0.0001756109,0.00002420409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003172926,"about_ca_system_score_gemma":0.00007942317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005010089,"about_ca_topic_score_gemma":0.0001196513,"domain_scores_codex":[0.9989135,0.0001260258,0.0002308216,0.0003606672,0.0001496124,0.0002193713],"domain_scores_gemma":[0.9992232,0.0001572653,0.00008027299,0.0003202035,0.0001674117,0.00005159139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002896742,0.0001317911,0.0000491585,0.00007071424,0.00001775074,0.000008702299,0.0001457889,0.62274,0.02091762,0.02042134,0.00615803,0.3293101],"study_design_scores_gemma":[0.0001603908,0.00005240212,0.00005743393,0.00002049746,0.000005085674,0.00001061285,0.000004078928,0.9246035,0.0372863,0.001695328,0.03598212,0.0001222071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001043411,0.0002177629,0.9958888,0.001371407,0.0004537871,0.0002709494,7.633336e-7,0.0003442104,0.0004089232],"genre_scores_gemma":[0.5529997,0.00001769417,0.444313,0.001101414,0.0003068727,0.0001605729,0.00003687714,0.00001436249,0.001049486],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5519563,"threshold_uncertainty_score":0.403176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0169427785981852,"score_gpt":0.2417418556943168,"score_spread":0.2247990770961316,"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."}}