{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001486756,0.0008429536,0.001188876,0.0007774152,0.0005515298,0.001067145,0.001427575,0.0008647455,0.0004204884],"category_scores_gemma":[0.002790957,0.0002621388,0.0007266063,0.000651042,0.0006782952,0.001919936,0.001410992,0.001044662,0.0001291525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000974086,"about_ca_system_score_gemma":0.001025274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003428543,"about_ca_topic_score_gemma":0.003060263,"domain_scores_codex":[0.999167,0.0002251277,0.00005977802,0.0002334023,0.0001818189,0.0001330289],"domain_scores_gemma":[0.998936,0.0003675684,0.000152434,0.0002544094,0.0002119166,0.00007769028],"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":[0.000419365,0.0005153638,0.009853302,0.00007684297,0.0001457561,0.0002701826,0.0001105092,0.7802078,0.00444892,0.005323602,0.002288033,0.1963403],"study_design_scores_gemma":[0.000004602841,0.0000327632,0.0002927751,0.000003479112,0.000006191486,0.00002843966,0.00001289176,0.9959996,0.001243499,0.002214806,0.0001563989,0.000004555147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1879107,0.0006938826,0.8044181,0.0006643367,0.00009846652,0.00012193,0.0001416682,0.003638574,0.00231232],"genre_scores_gemma":[0.9483322,0.0001186168,0.0505718,0.000211627,0.00001450028,0.00005329512,0.0001374477,0.00002252602,0.0005380154],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003428543,"threshold_uncertainty_score":0.007862866,"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."}}