{"id":"W4386010817","doi":"10.5267/j.ijdns.2023.7.021","title":"Botnet attacks detection in IoT environment using machine learning techniques","year":2023,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Noise (video); Filter (signal processing); Botnet; Precision and recall; Process (computing); Resilience (materials science); Feature (linguistics); Construct (python library); Noise reduction; Data mining; The Internet","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009998193,0.0009613535,0.001003271,0.00294226,0.0004907645,0.0009520498,0.0007589528,0.000847647,0.0005780793],"category_scores_gemma":[0.002335787,0.0002354526,0.0009046066,0.001502363,0.000286214,0.001120173,0.0006165195,0.000956642,0.0006272741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000700027,"about_ca_system_score_gemma":0.00061221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002972858,"about_ca_topic_score_gemma":0.003029348,"domain_scores_codex":[0.9989134,0.0001877447,0.0001083739,0.000311816,0.000334658,0.0001440193],"domain_scores_gemma":[0.9986957,0.0004498879,0.0003036732,0.0001515882,0.0003378521,0.00006128691],"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":[0.000390919,0.001037467,0.04564583,0.000481151,0.0002738925,0.0008778993,0.0003195493,0.1951195,0.02865881,0.002742101,0.0156432,0.7088097],"study_design_scores_gemma":[0.000008952503,0.0001271903,0.00746101,0.00002889079,0.00002709128,0.0001936833,0.00009456003,0.9777594,0.009908984,0.001895714,0.002473489,0.00002110475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5137153,0.002430241,0.4572035,0.001388397,0.0004410591,0.0004294734,0.002506828,0.01066691,0.01121824],"genre_scores_gemma":[0.8015476,0.0005392674,0.1911494,0.0002255015,0.00007791442,0.0001544491,0.00309783,0.00008857687,0.003119601],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002972858,"threshold_uncertainty_score":0.005911112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0366938336299679,"score_gpt":0.3117742320087345,"score_spread":0.2750803983787666,"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."}}