{"id":"W4388494856","doi":"10.18280/ria.370505","title":"Smart Intrusion Detection in IoT Edge Computing Using Federated Learning","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Internet of Things; Edge computing; Intrusion detection system; Enhanced Data Rates for GSM Evolution; Intrusion prevention system; Computer security; Embedded system; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001068661,0.000181382,0.0002153425,0.0004900614,0.0007405975,0.000251758,0.0003930069,0.0001438686,0.00004316965],"category_scores_gemma":[0.0001936681,0.0002026194,0.00007957239,0.00333538,0.00004978929,0.000314716,0.0003676522,0.000531273,0.0005804163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001414999,"about_ca_system_score_gemma":0.00003677408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001973011,"about_ca_topic_score_gemma":0.0001547994,"domain_scores_codex":[0.9979386,0.0002224913,0.0005412702,0.0005721652,0.0002163868,0.0005090387],"domain_scores_gemma":[0.9991463,0.000204972,0.000154817,0.0003046986,0.0001034797,0.00008567674],"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":[0.00001361178,0.00004537101,0.0003726097,0.00002842299,0.000004644245,0.00003245646,0.001791957,0.4989232,0.04156949,0.0008310897,0.0000332814,0.4563539],"study_design_scores_gemma":[0.00004293773,0.00008553611,0.0002562286,0.0001270458,0.000002057457,0.00003395137,0.0003515687,0.8915079,0.1048593,0.0008979673,0.00163206,0.0002033916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4985206,0.00004648173,0.4996122,0.0001174936,0.000819572,0.0001402085,1.405848e-7,0.0003748751,0.0003684962],"genre_scores_gemma":[0.9979587,0.00005747323,0.001497162,0.00006080942,0.0001632266,0.000005720793,0.000002961367,0.00001756821,0.0002364112],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4994381,"threshold_uncertainty_score":0.8262581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04453726713671636,"score_gpt":0.2773256718634286,"score_spread":0.2327884047267123,"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."}}