{"id":"W4389211950","doi":"10.54808/jsci.21.03.29","title":"Using Federated Learning for Collaborative Intrusion Detection Systems","year":2023,"lang":"en","type":"article","venue":"Journal of systemics, cybernetics, and informatics/Journal of systemics cybernetics and informatics","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Federated learning; Implementation; Artificial neural network; Intrusion detection system; Machine learning; Artificial intelligence; Convergence (economics); Training set; Enhanced Data Rates for GSM Evolution; Data mining; Computer security","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00536936,0.0005243901,0.001434989,0.001284341,0.0006394141,0.00152336,0.0007200595,0.0005058419,0.000001657452],"category_scores_gemma":[0.0006229072,0.0004442318,0.000265347,0.001051703,0.0001777946,0.002862683,0.0003353687,0.001008279,0.000005992562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000348497,"about_ca_system_score_gemma":0.0004057172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002267549,"about_ca_topic_score_gemma":0.000006906851,"domain_scores_codex":[0.991891,0.0002420723,0.005927666,0.0001547307,0.001165272,0.0006193017],"domain_scores_gemma":[0.9860791,0.0005609597,0.008444411,0.0003069388,0.00413678,0.0004718109],"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":[0.001858671,0.0004866662,0.004614105,0.03396438,0.003864787,0.0001561218,0.2772439,0.482477,0.009317438,0.04886273,0.01264281,0.1245114],"study_design_scores_gemma":[0.002396927,0.001898631,0.00006914869,0.003604203,0.000202852,0.00471485,0.03020874,0.9429829,0.0008363611,0.0006068568,0.01194378,0.0005348241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5443106,0.003724089,0.4451179,0.00009282304,0.005168249,0.000996899,0.00002197257,0.00007493528,0.00049247],"genre_scores_gemma":[0.9725345,0.0113307,0.01502082,0.00009431667,0.0008493584,0.000006651436,0.00000703248,0.00004916302,0.0001074551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4605058,"threshold_uncertainty_score":0.9998009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02129340653113931,"score_gpt":0.241210358337592,"score_spread":0.2199169518064527,"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."}}