{"id":"W4390675884","doi":"10.36227/techrxiv.170475326.65758197/v1","title":"Enhancing Network Intrusion Detection: An AutoML Pipeline with Efficient Digital Twin Synchronization","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Pipeline (software); Intrusion detection system; Computer science; Synchronization (alternating current); Intrusion; Computer network; Data mining; Geology; Operating system","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005482999,0.0004968653,0.0003878261,0.0002867004,0.0003803171,0.002039194,0.0008410497,0.0004421305,0.00009669849],"category_scores_gemma":[0.00002989208,0.0004082421,0.0001308686,0.001207716,0.00006362992,0.0004976222,0.00275931,0.001239005,0.00019272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003302512,"about_ca_system_score_gemma":0.0002699953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007062303,"about_ca_topic_score_gemma":0.0004382573,"domain_scores_codex":[0.9966437,0.0001178547,0.0006152957,0.001405039,0.0006898139,0.0005282575],"domain_scores_gemma":[0.9980549,0.00006711903,0.000250662,0.001136498,0.0002758584,0.0002149544],"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":[0.00005042559,0.0001361783,0.000008938706,0.0002021435,0.00004548901,0.00004388798,0.0007318764,0.6265891,0.0001617135,0.004592845,0.0006251953,0.3668122],"study_design_scores_gemma":[0.0001652734,0.0003229746,0.00002498978,0.0005773754,0.00003196494,0.0000880487,0.00002423904,0.9844417,0.00336317,0.007818508,0.002583703,0.0005580747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0419783,0.0004428207,0.947858,0.0002879278,0.004653243,0.0005484229,0.000004138742,0.001959528,0.002267612],"genre_scores_gemma":[0.9835597,0.00003911571,0.01313775,0.0001858478,0.002419256,0.00005177413,0.00005012105,0.00005316599,0.0005033029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9415814,"threshold_uncertainty_score":0.9998369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006975394030866253,"score_gpt":0.2194985801125985,"score_spread":0.2125231860817323,"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."}}