{"id":"W7106634211","doi":"10.1016/j.procs.2025.10.196","title":"A Comparative Study to Feature Selection for Network Security, By using Deep Learning as an Embedded Model","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski; HEC Montréal; Université du Québec à Montréal","funders":"Université du Québec à Rimouski","keywords":"Singular value decomposition; Benchmark (surveying); Feature selection; Deep learning; Principal component analysis; Curse of dimensionality; Feature (linguistics); Component (thermodynamics); Selection (genetic algorithm)","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001051205,0.0002147125,0.0002494258,0.0002759005,0.001215914,0.00106608,0.001344764,0.00005671367,4.333498e-7],"category_scores_gemma":[0.0000607873,0.0002169329,0.00004362416,0.003125201,0.00006697527,0.001528065,0.000519637,0.0002828982,0.000003900411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000164928,"about_ca_system_score_gemma":0.0003517509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000380071,"about_ca_topic_score_gemma":0.0000764388,"domain_scores_codex":[0.9976732,0.00008554187,0.0002049189,0.001042399,0.0004522558,0.0005416762],"domain_scores_gemma":[0.9987934,0.00008952589,0.0001047876,0.0003169113,0.0005017991,0.0001935774],"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":[0.00006700378,0.0004224432,0.002564941,0.00002985964,0.00002713984,8.834092e-7,0.03662765,0.9324692,0.003099543,0.003829858,0.002679418,0.01818205],"study_design_scores_gemma":[0.0002890624,0.0007503063,0.0002117398,0.00002809613,0.00001102502,0.000006624185,0.000130782,0.987792,0.001949089,0.008476169,0.0001177628,0.0002373057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3012694,0.00002911003,0.696934,0.0001373112,0.0006349045,0.0006907983,3.732218e-7,0.0002345649,0.00006957255],"genre_scores_gemma":[0.7872698,5.39351e-7,0.2118953,0.0005470178,0.0001781231,0.00005286186,0.000001037253,0.000006231903,0.00004911114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4860004,"threshold_uncertainty_score":0.9999709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01988181705804715,"score_gpt":0.3185802084535893,"score_spread":0.2986983913955422,"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."}}