{"id":"W7152533239","doi":"10.1109/icecet63943.2025.11472327","title":"Detecting Computer Network Intrusions using Machine Learning Algorithms","year":2025,"lang":"","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Support vector machine; Deep learning; Feature (linguistics); Pattern recognition (psychology); Feature selection","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":[0.001218255,0.001065223,0.001190312,0.004080717,0.0006219264,0.00176254,0.001041704,0.00127016,0.0008366083],"category_scores_gemma":[0.00538554,0.0003595959,0.0006931121,0.001566019,0.0004683893,0.002300574,0.0006770816,0.001329134,0.0005582192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006448799,"about_ca_system_score_gemma":0.0007052836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002140806,"about_ca_topic_score_gemma":0.00286187,"domain_scores_codex":[0.9985201,0.0002449031,0.0001417823,0.00032887,0.0006032122,0.0001611629],"domain_scores_gemma":[0.9960402,0.001948002,0.0006967354,0.0003333048,0.0008667411,0.000114872],"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":[0.0004255431,0.001439682,0.07858703,0.0002438191,0.0004165847,0.0003589015,0.0001048285,0.1644601,0.02313137,0.00355523,0.005659244,0.7216177],"study_design_scores_gemma":[0.00001484203,0.0001085421,0.004603777,0.0000181398,0.00004416424,0.000193751,0.00003778198,0.9834533,0.007586221,0.003010047,0.0009152601,0.00001417309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3551787,0.001865059,0.6310827,0.0008954906,0.0002991056,0.0003007246,0.0004828314,0.004764075,0.00513129],"genre_scores_gemma":[0.8424267,0.0005127952,0.1535564,0.0001833878,0.0001519664,0.00008377494,0.0007958371,0.00006983455,0.002219282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004080717,"threshold_uncertainty_score":0.006442845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01915526079217286,"score_gpt":0.2597671374545674,"score_spread":0.2406118766623946,"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."}}