{"id":"W3131825464","doi":"10.1007/s00607-021-00912-2","title":"Adaptive ensembles of autoencoders for unsupervised IoT network intrusion detection","year":2021,"lang":"en","type":"article","venue":"Computing","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Intrusion detection system; Anomaly detection; Scalability; Artificial intelligence; Machine learning; Inference; Cloud computing; Enhanced Data Rates for GSM Evolution; Artificial neural network; Internet of Things; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000947101,0.0006964619,0.0008705673,0.0005327775,0.0002915231,0.0005361665,0.0008343084,0.0007173219,0.0007392583],"category_scores_gemma":[0.003124694,0.0005071322,0.0006878784,0.0005242242,0.0003549788,0.0009967249,0.0008470106,0.00149061,0.0003328025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004381306,"about_ca_system_score_gemma":0.0005326998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003498147,"about_ca_topic_score_gemma":0.006688836,"domain_scores_codex":[0.9994603,0.0001409112,0.00003746224,0.0001389594,0.0001382944,0.00008408908],"domain_scores_gemma":[0.998588,0.0006723718,0.0001099722,0.000200913,0.0003812723,0.00004741692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002025178,0.0001880017,0.00260203,0.00005518301,0.0002420071,0.00007077433,0.00009184752,0.7018408,0.01141524,0.003793957,0.002454773,0.2770429],"study_design_scores_gemma":[0.000001431332,0.00001104984,0.0002791856,0.000002354018,0.000007873571,0.000008375595,0.000003700743,0.998047,0.0008402974,0.0006833636,0.000112944,0.00000242088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0590035,0.0004365305,0.9382798,0.0001406696,0.0001256483,0.00002516256,0.00008503952,0.0007338697,0.001169815],"genre_scores_gemma":[0.8386728,0.0003120459,0.1567305,0.000150986,0.0001379631,0.00007469987,0.0004055635,0.0001312464,0.00338424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003498147,"threshold_uncertainty_score":0.006955564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02195650726565292,"score_gpt":0.2399702837364764,"score_spread":0.2180137764708234,"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."}}