{"id":"W2904885747","doi":"10.17762/ijcnis.v10i3.3624","title":"BotCap: Machine Learning Approach for Botnet Detection Based on Statistical Features","year":2022,"lang":"en","type":"article","venue":"International Journal of Communication Networks and Information Security (IJCNIS)","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Botnet; Computer science; Artificial intelligence; Network packet; Machine learning; Set (abstract data type); Deep packet inspection; Data mining; Computer security; The Internet; World Wide Web","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":[0.001005283,0.001376646,0.0009541193,0.002988232,0.0006227805,0.0008291384,0.001562427,0.001413137,0.002660498],"category_scores_gemma":[0.002213225,0.000499562,0.0007476268,0.00140587,0.0004892579,0.001404029,0.001049664,0.001676944,0.001239212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006574892,"about_ca_system_score_gemma":0.0008036489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001797483,"about_ca_topic_score_gemma":0.002348991,"domain_scores_codex":[0.9993083,0.0001312714,0.00003299189,0.000156076,0.0003024798,0.00006881486],"domain_scores_gemma":[0.9989361,0.0004849382,0.0001229975,0.0001652163,0.0002299114,0.0000607802],"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":[0.0002724566,0.001044888,0.009530293,0.0004048781,0.0004052279,0.0004102435,0.0001308425,0.1541245,0.0316227,0.01187198,0.0185196,0.7716624],"study_design_scores_gemma":[0.00001478324,0.00007895518,0.00137119,0.00001206479,0.00002098958,0.0001543675,0.00001371329,0.9807165,0.007785032,0.005968274,0.003841467,0.00002252799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01216642,0.0002364809,0.9726283,0.0001488122,0.00006966077,0.0001771464,0.0004098652,0.01268418,0.001479043],"genre_scores_gemma":[0.3030904,0.0002868104,0.6882873,0.0002958518,0.0001307687,0.0004488549,0.001709568,0.0005492422,0.005201272],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002988232,"threshold_uncertainty_score":0.008900225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007643147577651825,"score_gpt":0.2393358168784427,"score_spread":0.2316926693007909,"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."}}