{"id":"W4379649966","doi":"10.36227/techrxiv.23284259","title":"AI for Anomaly Detection in IoT","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Internet of Things; Anomaly detection; Computer science; Safeguarding; Computer security; Intrusion detection system; Constant false alarm rate; Convolutional neural network; Artificial intelligence; Deep learning; Scheme (mathematics); Data science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001098309,0.0007324478,0.0007977997,0.001932059,0.0005241944,0.001470755,0.001214379,0.001257638,0.004477874],"category_scores_gemma":[0.005567409,0.0003086801,0.000875915,0.002130453,0.0007748123,0.001853248,0.001239585,0.002034975,0.001314439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009408628,"about_ca_system_score_gemma":0.0009181643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004651377,"about_ca_topic_score_gemma":0.002753349,"domain_scores_codex":[0.9989169,0.0001929876,0.00007548867,0.0003196362,0.0003907614,0.0001043867],"domain_scores_gemma":[0.9980361,0.001056929,0.0001636266,0.0002668998,0.0004130643,0.00006342922],"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":[0.0002247228,0.0002284906,0.009284507,0.0004175029,0.0001889851,0.0003971868,0.0001242425,0.2684157,0.01119498,0.08140109,0.02135375,0.6067688],"study_design_scores_gemma":[0.000005178983,0.00002187498,0.0007970237,0.00001495293,0.0000100097,0.0001155239,0.00001634733,0.9593569,0.001527381,0.03378621,0.004337922,0.0000106187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01437173,0.002806866,0.9699812,0.001537565,0.0005315349,0.00009872932,0.0005396114,0.002914232,0.007218568],"genre_scores_gemma":[0.6221805,0.003319153,0.354712,0.0009608782,0.0009224976,0.000352288,0.002249541,0.0003610159,0.01494197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004651377,"threshold_uncertainty_score":0.01498002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03304208794250257,"score_gpt":0.2785269723955549,"score_spread":0.2454848844530523,"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."}}