{"id":"W4408067816","doi":"10.1007/978-981-96-0147-9_44","title":"Machine Learning-Driven Security Information and Event Management (SIEM)","year":2025,"lang":"en","type":"book-chapter","venue":"Smart innovation, systems and technologies","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Event (particle physics); Computer science; Information security; Event management; Security information and event management; Computer security; Knowledge management; Cloud computing security; Operating system; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003770054,0.0002681202,0.0003031858,0.0009903893,0.000325803,0.0003282218,0.0003343345,0.0004341827,0.000003702801],"category_scores_gemma":[0.00004035847,0.0002495284,0.00003047832,0.000438242,0.000100695,0.0005935264,0.0007911084,0.0004998986,0.00001028205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006402647,"about_ca_system_score_gemma":0.00002613824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004412838,"about_ca_topic_score_gemma":0.00001430163,"domain_scores_codex":[0.9985833,0.00001821689,0.0006227604,0.0003483789,0.0002567219,0.0001706212],"domain_scores_gemma":[0.9987998,0.00003169493,0.0004542953,0.0004150386,0.0002838175,0.00001536136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003050149,0.0000040021,0.0000864472,0.000324537,0.00005108053,0.000002193657,0.00007253007,0.00001773596,9.344923e-7,0.8889775,0.002234604,0.1082253],"study_design_scores_gemma":[0.0002061887,0.0001029502,0.00005905941,0.0004625202,0.00001666967,0.00002406222,0.00009970825,0.02018346,0.00001885777,0.1031973,0.8753495,0.0002797335],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.001645379,0.01935106,0.3458953,0.006957327,0.005436054,0.004432082,0.00009867924,0.008215656,0.6079684],"genre_scores_gemma":[0.7723716,0.03567084,0.005998309,0.000515621,0.0002563982,0.0004042219,0.0004667689,0.00006104697,0.1842552],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8731149,"threshold_uncertainty_score":0.9999957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007993684609785303,"score_gpt":0.2033983459189336,"score_spread":0.1954046613091483,"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."}}