{"id":"W4383860742","doi":"10.36227/techrxiv.23620674","title":"Optimized Lightweight Federated Learning for Botnet Detection in Smart Critical Infrastructure","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Botnet; Hyperparameter; Enhanced Data Rates for GSM Evolution; Edge computing; Dimensionality reduction; Artificial intelligence; Computation; Oversampling; Deep learning; Feature (linguistics); Machine learning; Edge device; Data mining; Computer network; Algorithm; Operating system; The Internet","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001317612,0.0008383596,0.001016949,0.0006391667,0.0003825747,0.0007524531,0.001551134,0.001088695,0.001633114],"category_scores_gemma":[0.003858672,0.0003740164,0.0004692218,0.0005324396,0.0007375684,0.001606001,0.001497756,0.001152512,0.0005336193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001122479,"about_ca_system_score_gemma":0.00127358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004565056,"about_ca_topic_score_gemma":0.00500073,"domain_scores_codex":[0.9993342,0.0001765824,0.00003583901,0.0001640255,0.0001592668,0.0001300224],"domain_scores_gemma":[0.9990218,0.0003891315,0.0001070658,0.0001922565,0.0002228331,0.00006694026],"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":[0.0003482045,0.0002463635,0.002802013,0.00006475017,0.00006914022,0.00009394887,0.00006081452,0.6680545,0.008865277,0.00446032,0.00352575,0.3114089],"study_design_scores_gemma":[0.000004385014,0.00001152768,0.00007704124,0.000001287974,0.000001675814,0.000006774513,0.000002749588,0.9979163,0.0007658738,0.001132153,0.0000786279,0.000001619047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0462161,0.0001781482,0.9498411,0.0002274369,0.00003855003,0.00003860087,0.00009302859,0.002616398,0.0007506007],"genre_scores_gemma":[0.8049585,0.00008214957,0.1915217,0.0002851676,0.00004449275,0.0001038672,0.000385275,0.000162256,0.002456507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004565056,"threshold_uncertainty_score":0.009076953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01916642386491446,"score_gpt":0.270590588324625,"score_spread":0.2514241644597105,"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."}}