{"id":"W4410341398","doi":"10.1109/icnc64010.2025.10994022","title":"HCL: A Hybrid CNN-LSTM Framework for Intrusion Detection in SDN-IoT Networks","year":2025,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Intrusion detection system; Internet of Things; Computer network; Computer security; Software-defined networking; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004831135,0.0008425849,0.000417351,0.0004935751,0.0002000289,0.0005247951,0.001170965,0.0007434757,0.001585732],"category_scores_gemma":[0.0008445829,0.0003018542,0.0004459209,0.0003466835,0.0002838679,0.0008853779,0.000720984,0.0008898536,0.0004603768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007629741,"about_ca_system_score_gemma":0.0008789892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009450263,"about_ca_topic_score_gemma":0.01388261,"domain_scores_codex":[0.9998322,0.00002516507,0.000009216116,0.00004710762,0.00004527523,0.00004102195],"domain_scores_gemma":[0.9998364,0.00004872348,0.00002165418,0.0000152825,0.0000618385,0.00001602949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002110485,0.0001771225,0.002652591,0.0001451067,0.0001969197,0.0001739473,0.00006357211,0.5938914,0.02236241,0.005655375,0.006486325,0.3679842],"study_design_scores_gemma":[0.000003144786,0.00002154875,0.0001455653,0.000004797773,0.00000833777,0.00001346732,0.000004014639,0.9964109,0.001999671,0.0009568284,0.0004275952,0.000004103057],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03186496,0.0008285856,0.9581502,0.0003506706,0.0001414936,0.00007271349,0.00035862,0.005707625,0.002525119],"genre_scores_gemma":[0.7524607,0.0006395594,0.2377338,0.0005268431,0.00009403559,0.0001599711,0.001141588,0.0002231709,0.007020352],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009450263,"threshold_uncertainty_score":0.01879048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008785191710696194,"score_gpt":0.2512156492632858,"score_spread":0.2424304575525896,"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."}}