{"id":"W4379649965","doi":"10.36227/techrxiv.23284259.v1","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":0,"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":[0.001352352,0.0007920397,0.0008067042,0.002104829,0.0005252098,0.001559955,0.00132301,0.001351145,0.00453158],"category_scores_gemma":[0.007019702,0.0003287372,0.0009003304,0.002256767,0.0008676429,0.001894852,0.001278639,0.002315994,0.001364413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000878298,"about_ca_system_score_gemma":0.0009943481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003608056,"about_ca_topic_score_gemma":0.002350395,"domain_scores_codex":[0.9987106,0.0002558716,0.00009109949,0.0003594451,0.0004707352,0.0001122608],"domain_scores_gemma":[0.9975085,0.001427652,0.0002126267,0.0003270853,0.0004545138,0.00006955872],"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":[0.0002279817,0.0002287959,0.009506254,0.0005572478,0.0002380533,0.0004158797,0.000148555,0.2575287,0.01104557,0.1212549,0.02095352,0.5778946],"study_design_scores_gemma":[0.0000065016,0.00002776521,0.0008523065,0.00002413733,0.00001366257,0.0001616772,0.00002030413,0.9372128,0.001774912,0.05304047,0.006851303,0.00001403379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009897197,0.002726714,0.9763248,0.001354691,0.0004775148,0.00008957644,0.0004304317,0.002456606,0.006242554],"genre_scores_gemma":[0.548943,0.003963628,0.4286306,0.001006731,0.001082193,0.0004054043,0.002059131,0.0003939523,0.01351535],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00453158,"threshold_uncertainty_score":0.01515967,"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."}}