{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001599265,0.0001467478,0.0001939806,0.0003899827,0.0006784166,0.0004352788,0.001053025,0.00007879184,0.00004444031],"category_scores_gemma":[0.0001537752,0.000144308,0.0001099593,0.0002634134,0.00005298747,0.002109369,0.0003254978,0.0009021794,0.000001125248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001489309,"about_ca_system_score_gemma":0.00005731825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002149567,"about_ca_topic_score_gemma":0.000005053482,"domain_scores_codex":[0.9979163,0.0003613291,0.0006975834,0.0001267722,0.0007341501,0.0001638526],"domain_scores_gemma":[0.9977854,0.0004200482,0.0008439593,0.0002777288,0.0005802848,0.00009256238],"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":[0.0006771528,0.0001935421,0.00006279954,0.00001276486,0.00006961672,0.000001191821,0.0008803137,0.7936733,0.00001069705,0.07823601,0.003553307,0.1226293],"study_design_scores_gemma":[0.001021816,0.0004861345,0.0003551831,0.00001555103,0.000009762608,0.0001033601,0.00009996922,0.8610536,0.00004420337,0.001690299,0.1349938,0.0001262697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001173425,0.0004520715,0.9940563,0.001948365,0.000722885,0.0002352559,0.00002705297,0.00005326517,0.001331378],"genre_scores_gemma":[0.9848188,0.000680918,0.01244093,0.001629242,0.0001579428,0.00004392315,0.0002096632,0.000007650517,0.00001094098],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9836454,"threshold_uncertainty_score":0.5884709,"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."}}