{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006118694,0.0003288507,0.000366592,0.0001591442,0.0005777133,0.000787098,0.0006929422,0.0005021848,0.0001940151],"category_scores_gemma":[0.00006134922,0.0003351086,0.0001955744,0.0006608213,0.00002706647,0.0004357061,0.003927152,0.001800476,0.00002139197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000182619,"about_ca_system_score_gemma":0.0001146021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009469972,"about_ca_topic_score_gemma":0.0005853882,"domain_scores_codex":[0.9974292,0.0004005522,0.0004378429,0.0009004773,0.0004061725,0.0004257633],"domain_scores_gemma":[0.9985683,0.00007036393,0.0003087927,0.0007573241,0.000180661,0.0001145564],"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":[0.00003137105,0.00008890206,0.0004709735,0.000140571,0.00008642583,0.00006852662,0.000724558,0.7311738,0.005739146,0.002515549,0.0001340946,0.2588262],"study_design_scores_gemma":[0.0001143102,0.00005679478,0.000105184,0.0001714263,0.00001764554,0.00006647596,0.00001338034,0.9858481,0.004775536,0.004621476,0.003828814,0.0003808792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1351696,0.001199443,0.8568192,0.000136365,0.004984411,0.0001960023,3.413354e-7,0.0006302372,0.0008645104],"genre_scores_gemma":[0.9155281,0.0004124593,0.08194091,0.0003989861,0.001400247,0.00001448362,0.000021972,0.00003075228,0.0002521012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7803586,"threshold_uncertainty_score":0.9999101,"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."}}