{"id":"W7115918670","doi":"10.32604/cmc.2025.074897","title":"A Comparative Benchmark of Machine and Deep Learning for Cyberattack Detection in IoT Networks","year":2025,"lang":"en","type":"article","venue":"Computers, materials & continua/Computers, materials & continua (Print)","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Benchmark (surveying); Deep learning; Metric (unit); Intrusion detection system; Internet of Things; Botnet; Selection (genetic algorithm); Precision and recall","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":[0.002383221,0.0009904287,0.002418218,0.0008628808,0.000481557,0.001472935,0.001539505,0.0005524343,0.0001067254],"category_scores_gemma":[0.0001277468,0.001048547,0.0002542601,0.0009224113,0.0003029774,0.0008668374,0.001626729,0.0004695946,0.00001949284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002056738,"about_ca_system_score_gemma":0.00008977585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005074722,"about_ca_topic_score_gemma":0.0003736321,"domain_scores_codex":[0.9929211,0.001264749,0.002437573,0.001765416,0.0004169906,0.001194155],"domain_scores_gemma":[0.9958763,0.001077724,0.001300783,0.001018018,0.000517106,0.0002101453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002972823,0.0008423586,0.001070904,0.001459413,0.0008897502,0.00008066807,0.006459071,0.008601871,0.7622179,0.01795489,0.003436596,0.1940138],"study_design_scores_gemma":[0.009662751,0.00166483,0.02737605,0.002728253,0.0002069332,0.0001198307,0.0001796381,0.3441639,0.585395,0.003552465,0.0224246,0.002525731],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5511428,0.0006360927,0.4394685,0.0002020049,0.006489525,0.001623789,0.00002019157,0.0003170297,0.0001000521],"genre_scores_gemma":[0.9742703,0.0002290774,0.023809,0.0003990585,0.0007850875,0.0002242862,0.0000840686,0.00005887647,0.0001402257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4231275,"threshold_uncertainty_score":0.9995636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008791581505577718,"score_gpt":0.2428821813819964,"score_spread":0.2340905998764187,"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."}}