{"id":"W4387951305","doi":"10.1109/ccece58730.2023.10288813","title":"Anomaly Detection for IoT Networks: Empirical Study","year":2023,"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":"Solana Networks (Canada); Dalhousie University","funders":"","keywords":"Anomaly detection; Novelty detection; Computer science; Local outlier factor; Novelty; Internet of Things; Leverage (statistics); Outlier; Encoder; Wearable computer; Artificial intelligence; Machine learning; Support vector machine; Data mining; World Wide Web; Embedded system","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":[],"consensus_categories":[],"category_scores_codex":[0.0004784997,0.00009127751,0.0001060033,0.0001325888,0.0002745868,0.0001314279,0.0003209848,0.00006478711,0.00001838663],"category_scores_gemma":[0.00003052507,0.00008036158,0.00006799616,0.001153566,0.00001160755,0.0001876743,0.0001486767,0.00009749169,0.0001068376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002608049,"about_ca_system_score_gemma":0.00001675321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002490443,"about_ca_topic_score_gemma":0.0002773314,"domain_scores_codex":[0.9989887,0.00006419363,0.0001803267,0.0003446335,0.0001586962,0.0002634392],"domain_scores_gemma":[0.9993836,0.0001442416,0.00003838933,0.0003100185,0.00006061475,0.00006308127],"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":[0.0001647018,0.0008061358,0.01015627,0.00002278261,0.0001143677,0.00002910301,0.002556508,0.02863683,0.0007278895,0.007293031,0.06468725,0.8848051],"study_design_scores_gemma":[0.0003553031,0.000722397,0.01303871,0.00000224557,0.000004702238,0.00000450997,0.00008196467,0.9675689,0.0004741192,0.002174322,0.01544661,0.0001261856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2681628,0.00001117817,0.7288989,0.0005109534,0.0009083374,0.000406149,1.546381e-7,0.0008060629,0.0002955273],"genre_scores_gemma":[0.9967813,0.000005579368,0.001742859,0.0003801424,0.0003536672,0.0000977162,8.764301e-7,0.000007726094,0.0006301576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9389321,"threshold_uncertainty_score":0.327705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03522645774244628,"score_gpt":0.3034002060602181,"score_spread":0.2681737483177719,"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."}}