{"id":"W4307811933","doi":"10.1049/cmu2.12530","title":"Retracted: Development of intrusion detection system using machine learning for the analytics of Internet of Things enabled enterprises","year":2022,"lang":"en","type":"article","venue":"IET Communications","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":8,"is_retracted":true,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Retract; Interim; Computer science; The Internet; Institution; Reliability (semiconductor); Peer review; Analytics; World Wide Web; Law; Data science; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":{"nature":"Retraction","reason":"Date of Article and/or Notice Unknown;Compromised Peer Review;Investigation by Journal/Publisher;Unreliable Results and/or Conclusions;","date":"10/28/2022 0:00","openalex_flagged":true},"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009236752,0.001310963,0.00121558,0.002793255,0.00142406,0.00440929,0.003851787,0.00244512,0.04108759],"category_scores_gemma":[0.04984787,0.0006909295,0.001125835,0.001243712,0.0009330773,0.005480377,0.003477669,0.003181142,0.03995158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000941577,"about_ca_system_score_gemma":0.003500071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002396492,"about_ca_topic_score_gemma":0.002308603,"domain_scores_codex":[0.9944137,0.001218842,0.0006597104,0.0006793364,0.002663084,0.0003652977],"domain_scores_gemma":[0.9689289,0.005560705,0.001061284,0.004613036,0.0179512,0.001884896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005183061,0.0002326064,0.008932368,0.0007141997,0.0001485701,0.004660439,0.001643144,0.002518402,0.01416193,0.004522611,0.5398439,0.4221035],"study_design_scores_gemma":[0.0001339581,0.0007497872,0.007775351,0.0004722787,0.0001758555,0.00517792,0.00101676,0.07868236,0.0507731,0.004336071,0.8504874,0.000219085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1324081,0.007597121,0.4477868,0.0651857,0.1168176,0.002801068,0.01211542,0.1307579,0.08453029],"genre_scores_gemma":[0.3349665,0.004499486,0.259978,0.008676683,0.01011423,0.0009250775,0.03462966,0.01657256,0.3296378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04108759,"threshold_uncertainty_score":0.1374517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03566159569684345,"score_gpt":0.2645653358967848,"score_spread":0.2289037401999413,"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."}}