{"id":"W4409841704","doi":"10.1016/j.adhoc.2025.103871","title":"SC-MLIDS: Fusion-based Machine Learning Framework for Intrusion Detection in Wireless Sensor Networks","year":2025,"lang":"en","type":"article","venue":"Ad Hoc Networks","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Norleaf Networks (Canada); Cistel Technology (Canada); Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intrusion detection system; Computer science; Wireless sensor network; Fusion; Artificial intelligence; Computer network","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":[0.003427761,0.000901808,0.001363426,0.001524159,0.0005726036,0.00110685,0.002280131,0.001053558,0.0008057568],"category_scores_gemma":[0.004033746,0.0003452677,0.001189292,0.001301747,0.001051493,0.00185663,0.001888332,0.00176038,0.0003660917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001300668,"about_ca_system_score_gemma":0.00152113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003760805,"about_ca_topic_score_gemma":0.003422842,"domain_scores_codex":[0.9977491,0.0007174987,0.0001285215,0.0003367546,0.000923521,0.0001444003],"domain_scores_gemma":[0.9986456,0.0005662386,0.0001450122,0.0001805212,0.0003984506,0.000064227],"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":[0.0001436909,0.0001555703,0.002115008,0.0001468498,0.0001977128,0.0001068537,0.0001921825,0.7048861,0.005861853,0.04316608,0.003391068,0.239637],"study_design_scores_gemma":[0.000004153776,0.00002879,0.0001000772,0.000004503994,0.000008999801,0.00002001162,0.000006452523,0.9906381,0.001015902,0.007201459,0.0009645582,0.000006926846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002549632,0.0001853662,0.9956813,0.0001144999,0.00002345217,0.00002969261,0.00002974527,0.0009943986,0.0003919157],"genre_scores_gemma":[0.4110729,0.0005124131,0.5857942,0.0002822774,0.0001255019,0.0002562776,0.0003689774,0.0001424143,0.001445038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003760805,"threshold_uncertainty_score":0.01812792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007260750987940545,"score_gpt":0.2364700472986495,"score_spread":0.229209296310709,"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."}}