{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006802871,0.0006573109,0.0005868727,0.001646101,0.0005481921,0.0008516289,0.001277164,0.0008473663,0.0007494907],"category_scores_gemma":[0.03407402,0.0002116318,0.0006246247,0.001943114,0.0009842598,0.002359306,0.0008545406,0.00158327,0.0002611246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008198079,"about_ca_system_score_gemma":0.0004268902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004962878,"about_ca_topic_score_gemma":0.004212135,"domain_scores_codex":[0.9966922,0.001558435,0.0002402063,0.0005802647,0.000717532,0.0002113582],"domain_scores_gemma":[0.9530241,0.03603987,0.003878596,0.0034941,0.003062004,0.000501227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005610422,0.001365342,0.7674155,0.0004263831,0.0004470985,0.0005065434,0.0004376432,0.1032784,0.001060573,0.004687833,0.01192231,0.1078913],"study_design_scores_gemma":[0.00004438649,0.0003692845,0.1510211,0.0001149061,0.0001274072,0.001146866,0.0008730854,0.8326724,0.001517197,0.007446826,0.004624148,0.00004234411],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765752,0.001832319,0.01819948,0.0007154437,0.00006477843,0.00005487376,0.001244955,0.0001744137,0.001138714],"genre_scores_gemma":[0.9936988,0.0004463666,0.003512065,0.00004501157,0.00005750154,0.00002340712,0.001994562,0.00001918427,0.0002030961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006802871,"threshold_uncertainty_score":0.03597748,"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."}}