{"id":"W4353046628","doi":"10.1109/icin56760.2023.10073491","title":"Soteria: An Approach for Detecting Multi-Institution Attacks","year":2023,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Canada; University of Waterloo","funders":"","keywords":"Institution; Computer science; Pipeline (software); Recall; Recall rate; Academic institution; Set (abstract data type); Precision and recall; Computer security; Data set; Artificial intelligence; Political science; Operating system; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002134167,0.002069994,0.001122673,0.009730525,0.00106336,0.002538904,0.002245572,0.001336128,0.003509158],"category_scores_gemma":[0.007858602,0.0007982825,0.001569183,0.00328671,0.000536854,0.003096308,0.002613772,0.002198109,0.004124973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00116243,"about_ca_system_score_gemma":0.002773681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01288633,"about_ca_topic_score_gemma":0.01942844,"domain_scores_codex":[0.9971826,0.0003045903,0.0002736871,0.0007073187,0.001150558,0.000381315],"domain_scores_gemma":[0.9951498,0.0009477716,0.0009472552,0.001330913,0.001278076,0.0003461052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001280108,0.001069648,0.1003276,0.0009282173,0.001092906,0.001039586,0.001472326,0.03367247,0.05167075,0.0101297,0.1568256,0.6404911],"study_design_scores_gemma":[0.0001066828,0.0005315396,0.03261135,0.0001397406,0.0002695482,0.001269116,0.0006789175,0.774864,0.06854241,0.01274156,0.1079806,0.0002645678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06276763,0.001052724,0.7156889,0.0007985551,0.0005810676,0.002143927,0.02072205,0.1841571,0.01208797],"genre_scores_gemma":[0.2631043,0.0005328663,0.6924857,0.0004956243,0.000180614,0.0008602082,0.03200908,0.001990537,0.008341197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01288633,"threshold_uncertainty_score":0.02562261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05777458073481707,"score_gpt":0.2952401506294201,"score_spread":0.237465569894603,"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."}}