{"id":"W3048313003","doi":"10.1109/tnsm.2020.3014929","title":"Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":274,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Intrusion detection system; Computer science; Oversampling; Artificial intelligence; Machine learning; Constant false alarm rate; Feature selection; Sample size determination; Network security; Data mining; Feature (linguistics); Dependency (UML); Mathematics; Statistics; Computer security","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":[0.002025382,0.001161803,0.001479696,0.0008838565,0.0003350967,0.001048742,0.001959237,0.001031577,0.001494684],"category_scores_gemma":[0.003228491,0.0008068102,0.001083774,0.0007822534,0.0006583761,0.00113512,0.001075372,0.001766683,0.0005023561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001169977,"about_ca_system_score_gemma":0.001668429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007383768,"about_ca_topic_score_gemma":0.007794261,"domain_scores_codex":[0.9990628,0.0003638471,0.00005542303,0.0002154629,0.0001893427,0.0001131224],"domain_scores_gemma":[0.9989416,0.0005767103,0.000113488,0.00007593453,0.0002505425,0.00004175302],"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":[0.00009233623,0.00006814332,0.001136252,0.00004487637,0.00008517477,0.00004673436,0.00002772426,0.9421392,0.001120903,0.003282121,0.0007025378,0.05125391],"study_design_scores_gemma":[0.000002264683,0.00001045671,0.00006132582,0.000001194616,0.000003757231,0.000004155153,0.000001065354,0.9991418,0.0001576882,0.0005334379,0.00008110099,0.000001770113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009515161,0.0003132421,0.988636,0.0001435586,0.00001950261,0.00005326494,0.0000579339,0.0008248333,0.0004364544],"genre_scores_gemma":[0.6087809,0.0003860683,0.3848016,0.0002964369,0.00009461796,0.0004721402,0.0005156926,0.0002002692,0.004452173],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007383768,"threshold_uncertainty_score":0.01468158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02953981615032476,"score_gpt":0.2499424353563529,"score_spread":0.2204026192060281,"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."}}