{"id":"W4415547480","doi":"10.1016/j.iot.2025.101802","title":"iPASecIoT: An intelligent pipeline for automatic and adaptive feature extraction for secure IoT device identification and intrusion detection","year":2025,"lang":"en","type":"article","venue":"Internet of Things","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research and Productivity Council; University of New Brunswick","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Scalability; Identification (biology); Intrusion detection system; Pipeline (software); Inference; Feature (linguistics); Obfuscation; Feature extraction","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":[0.001016347,0.002645192,0.001328393,0.002126291,0.0004600657,0.001016401,0.002053153,0.001161476,0.002844353],"category_scores_gemma":[0.002631259,0.0005637807,0.001893522,0.001069385,0.0003976306,0.001927274,0.001963821,0.001918128,0.00323046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006411389,"about_ca_system_score_gemma":0.00108412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003665187,"about_ca_topic_score_gemma":0.005678942,"domain_scores_codex":[0.9994119,0.00005466619,0.00003158621,0.0002289531,0.0001779545,0.00009489025],"domain_scores_gemma":[0.9995643,0.0001163339,0.00005357746,0.0001066864,0.0001232925,0.00003567749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000522688,0.0007208824,0.009684159,0.0002810198,0.0004227568,0.0004339303,0.0001475042,0.06224752,0.04654946,0.002460813,0.0518673,0.8246619],"study_design_scores_gemma":[0.00002656523,0.0001500955,0.001755567,0.00001620653,0.00003405384,0.000161773,0.00003126793,0.9754568,0.01353678,0.002994327,0.005808875,0.00002763746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03509073,0.0007797711,0.8953279,0.0003009156,0.0002096898,0.000344532,0.003173351,0.06288693,0.001886227],"genre_scores_gemma":[0.3076774,0.000556244,0.6560943,0.0005517469,0.0001185849,0.0009243987,0.02450995,0.001554074,0.008013405],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003665187,"threshold_uncertainty_score":0.009515345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01501305679513373,"score_gpt":0.2824437353645343,"score_spread":0.2674306785694006,"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."}}