{"id":"W4221146354","doi":"10.1109/tvt.2022.3193074","title":"Distributed Online Anomaly Detection for Virtualized Network Slicing Environment","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Chongqing Municipal Education Commission; National Natural Science Foundation of China","keywords":"Anomaly detection; Computer science; Anomaly (physics); Node (physics); Slicing; Distributed computing; Data mining; Real-time computing; Engineering","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0002909534,0.0002007306,0.0002397845,0.0003584391,0.001340044,0.0000361832,0.0005942977,0.0001927066,0.00007409049],"category_scores_gemma":[0.000005448868,0.0002273566,0.000180913,0.001109867,0.00006991949,0.0001556805,0.00002243843,0.0006465586,0.00001456182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000288166,"about_ca_system_score_gemma":0.00002614545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002070462,"about_ca_topic_score_gemma":0.00005089112,"domain_scores_codex":[0.9982642,0.0001260917,0.0003381451,0.0005759084,0.0002701032,0.0004255591],"domain_scores_gemma":[0.9990479,0.00008294696,0.000137061,0.0006376387,0.0000359799,0.00005842768],"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":[0.0001434111,0.0005196012,0.000005052597,0.000008934952,0.00009530441,0.00001455644,0.00006686459,0.758665,0.01686133,0.002905296,0.0001631247,0.2205516],"study_design_scores_gemma":[0.001471585,0.001960394,0.00003439233,0.00001319557,0.00005768179,0.0001660983,0.0001043499,0.8087086,0.0746409,0.006222743,0.1061815,0.0004385199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08374513,0.0001022002,0.9124314,0.001390384,0.001040942,0.0005178123,0.0000585666,0.0007077156,0.000005860608],"genre_scores_gemma":[0.9890157,0.00005643334,0.009805744,0.0003178856,0.00007151838,0.0006299076,0.00001804047,0.00002233453,0.00006246634],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9052705,"threshold_uncertainty_score":0.9999601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009721216181153226,"score_gpt":0.2136797835445797,"score_spread":0.2039585673634265,"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."}}