{"id":"W4285106051","doi":"10.1109/access.2022.3176317","title":"Design and Development of RNN Anomaly Detection Model for IoT Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":247,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Deep learning; Artificial intelligence; Recurrent neural network; Anomaly detection; Convolutional neural network; Machine learning; Intrusion detection system; Artificial neural network; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004272394,0.0000752472,0.0001007335,0.00008654561,0.0004311263,0.00007604429,0.0004543401,0.0000340341,0.000005679787],"category_scores_gemma":[0.000003982957,0.00008012351,0.00002191339,0.0002523116,0.00001470288,0.0002809882,0.0002305865,0.00009828709,3.027859e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004398588,"about_ca_system_score_gemma":0.00007224352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009676431,"about_ca_topic_score_gemma":0.00001942069,"domain_scores_codex":[0.9992114,0.00005532091,0.0001977209,0.0002339146,0.0001494113,0.0001522897],"domain_scores_gemma":[0.9995731,0.00006719696,0.0001107919,0.0001625495,0.00005048521,0.00003590257],"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":[0.00005745225,0.00003322657,0.00001648382,0.00001167918,0.00001071791,4.204484e-7,0.0006836993,0.8163577,0.001359537,0.000246281,0.0002891517,0.1809337],"study_design_scores_gemma":[0.0002101467,0.00008697675,0.00007525247,0.000003390466,0.000003017596,0.000005317059,0.000006188875,0.9742839,0.02239125,0.001825109,0.001013954,0.00009553532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07977631,0.00008105583,0.9193216,0.00004701861,0.000447933,0.0002653137,4.167516e-7,0.0000495798,0.00001076723],"genre_scores_gemma":[0.9230685,0.000007097406,0.07654776,0.000154542,0.00004415427,0.0001448859,5.714937e-7,0.000006062885,0.00002646317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8432922,"threshold_uncertainty_score":0.3315918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05390829886113963,"score_gpt":0.2737549412225625,"score_spread":0.2198466423614229,"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."}}