{"id":"W4236468428","doi":"10.32920/ryerson.14645952.v1","title":"Network intrusion detection using machine learning","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Machine learning; Artificial intelligence; Field (mathematics); Training set; Intrusion detection system; Set (abstract data type); Intrusion; Data mining; Data set; Test set","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002243593,0.001236872,0.001435495,0.004053,0.0005533205,0.002035385,0.001432423,0.001397622,0.001445008],"category_scores_gemma":[0.008603769,0.0005056151,0.001371498,0.002268537,0.0007443314,0.003323445,0.001244318,0.002066439,0.001160271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001149145,"about_ca_system_score_gemma":0.0007162996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001882236,"about_ca_topic_score_gemma":0.002325488,"domain_scores_codex":[0.9970193,0.001098997,0.000184374,0.0006610464,0.0008599528,0.000176423],"domain_scores_gemma":[0.9943209,0.00287238,0.0006856731,0.001166932,0.0008267473,0.0001273803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001722074,0.0007039228,0.02451608,0.0002908723,0.0005482857,0.0002000624,0.00013605,0.3338688,0.00669981,0.009222098,0.008363077,0.6152787],"study_design_scores_gemma":[0.000004877302,0.00005454961,0.001525757,0.00002061536,0.00001628461,0.0001029252,0.00002069814,0.9849854,0.003003953,0.008560875,0.001688438,0.00001563695],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.06211187,0.001614332,0.9223729,0.001273959,0.0002062081,0.0002250371,0.0006805578,0.006975844,0.004539346],"genre_scores_gemma":[0.673074,0.0009529036,0.3203912,0.0004272396,0.000244296,0.0001905458,0.001762222,0.000179863,0.002777753],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.004053,"threshold_uncertainty_score":0.01186538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02339080018799185,"score_gpt":0.2420214399602949,"score_spread":0.2186306397723031,"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."}}