{"id":"W4394875052","doi":"10.2139/ssrn.4797568","title":"Dns User Profiling and Risk Assessment: A Learning Approach","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Fredericton; University of New Brunswick; Université de Montréal","funders":"","keywords":"Profiling (computer programming); Computer science; Data science; Operating system","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":[0.003519463,0.001080926,0.001301531,0.002791475,0.0007038383,0.002136175,0.002172521,0.001500409,0.00225172],"category_scores_gemma":[0.01381905,0.0004802041,0.001230919,0.001788842,0.0008014518,0.002376836,0.001735542,0.002461299,0.0006533991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001037206,"about_ca_system_score_gemma":0.001294261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004242046,"about_ca_topic_score_gemma":0.00468947,"domain_scores_codex":[0.9984117,0.0006410735,0.0001251202,0.0004158366,0.0002917857,0.0001145145],"domain_scores_gemma":[0.9847348,0.01240341,0.0005756274,0.0007567274,0.001240242,0.0002892047],"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":[0.000351482,0.001213561,0.03511379,0.0001992991,0.0003444226,0.0002602976,0.0004533094,0.4120263,0.00155749,0.02336671,0.003776409,0.5213369],"study_design_scores_gemma":[0.000009300976,0.00006839478,0.0009567732,0.00001524504,0.00003115857,0.00004259343,0.00005828019,0.9809088,0.0005848136,0.01696378,0.0003490395,0.00001190379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04778048,0.0002928682,0.9479945,0.0005889055,0.00002455047,0.0001133941,0.000283089,0.000768563,0.0021536],"genre_scores_gemma":[0.7275275,0.0003651826,0.2681773,0.0001642928,0.0001191922,0.0001930593,0.0006634425,0.0000459153,0.002744192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004242046,"threshold_uncertainty_score":0.01861292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008825787123917479,"score_gpt":0.2498942771288047,"score_spread":0.2410684900048872,"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."}}