{"id":"W3217504530","doi":"10.1109/isncc52172.2021.9615638","title":"API Security in Large Enterprises: Leveraging Machine Learning for Anomaly Detection","year":2021,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Bank of Canada","funders":"","keywords":"Anomaly detection; Computer science; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0004347641,0.0001042119,0.0001346801,0.0001209262,0.0002172369,0.0001519818,0.0001999744,0.00006952044,0.00008227536],"category_scores_gemma":[0.00009070546,0.0001093609,0.00007985796,0.0005028612,0.00000728508,0.0005103006,0.0002070256,0.0002618806,0.00001353448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006431017,"about_ca_system_score_gemma":0.00003134903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001177364,"about_ca_topic_score_gemma":0.001309571,"domain_scores_codex":[0.9988586,0.0001190784,0.0002153051,0.0003844509,0.0001353051,0.0002872414],"domain_scores_gemma":[0.9995099,0.00009294945,0.0000568878,0.0002124801,0.00007777788,0.0000500485],"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":[0.0003952684,0.002041504,0.08225464,0.0005056319,0.0001675385,0.0003295627,0.02482606,0.008781599,0.06005116,0.07535517,0.001138802,0.7441531],"study_design_scores_gemma":[0.0007092963,0.00009073309,0.0012765,0.00002521219,0.000002825421,0.00003466822,0.00009434022,0.9188379,0.03892175,0.004251279,0.03558572,0.0001697717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1870992,0.0002386025,0.8106207,0.0002205004,0.000478577,0.0001210715,8.080261e-7,0.0001677997,0.001052742],"genre_scores_gemma":[0.9954336,0.0000557995,0.003718648,0.0003511357,0.00008877491,0.00002183504,0.000005173959,0.000007265781,0.0003177368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9100563,"threshold_uncertainty_score":0.4459608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01047807652373518,"score_gpt":0.2314213916445139,"score_spread":0.2209433151207787,"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."}}