{"id":"W4205357471","doi":"10.1109/idsta53674.2021.9660800","title":"Using Machine Learning for malware traffic prediction in IoT networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Malware; Computer science; Internet of Things; Computer network; Artificial intelligence; Computer security; Machine learning","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":[0.0009993511,0.001161871,0.0007206742,0.00359343,0.0005173365,0.0009161784,0.0006301692,0.0009244098,0.0004545013],"category_scores_gemma":[0.00330451,0.0002967691,0.0006838389,0.001448131,0.0003017799,0.001154387,0.0004663132,0.0009543574,0.0004894779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008636975,"about_ca_system_score_gemma":0.0005292131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006825719,"about_ca_topic_score_gemma":0.004973465,"domain_scores_codex":[0.9993556,0.000153113,0.00006038676,0.0001565335,0.0001335339,0.0001407888],"domain_scores_gemma":[0.9981427,0.001070729,0.0002131301,0.000140104,0.0003408707,0.00009246746],"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":[0.0005724849,0.001449251,0.1004516,0.0001480501,0.0002179732,0.0004042213,0.0001086106,0.6022381,0.004882545,0.001190198,0.007214052,0.2811228],"study_design_scores_gemma":[0.000002656955,0.00002641573,0.002226095,0.000006583438,0.000005730967,0.00002488339,0.00001939991,0.9958313,0.001023215,0.0006075072,0.0002213965,0.000004810978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8917704,0.001518746,0.09514967,0.001064567,0.0002828709,0.0001686669,0.002170441,0.003333386,0.004541347],"genre_scores_gemma":[0.976683,0.0002773396,0.01985492,0.00007790322,0.00007440578,0.00005280015,0.001953405,0.00003093983,0.000995308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006825719,"threshold_uncertainty_score":0.01357198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02629600288021537,"score_gpt":0.2492783050027674,"score_spread":0.222982302122552,"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."}}