{"id":"W4411949928","doi":"10.1109/iwcmc65282.2025.11059562","title":"Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Polytechnique Montréal","funders":"","keywords":"Anomaly detection; Multivariate statistics; Series (stratigraphy); Computer science; Time series; Anomaly (physics); Data mining; Artificial intelligence; Pattern recognition (psychology); Machine learning; Geology; 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":[],"consensus_categories":[],"category_scores_codex":[0.000199851,0.0001059906,0.0001136941,0.00007665015,0.0004253873,0.0001713181,0.0003396837,0.00005764501,0.000005093787],"category_scores_gemma":[0.00002179169,0.0000885271,0.00001641583,0.0004970524,0.00003624454,0.0008231323,0.0002340314,0.00006874132,0.000003151561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000248636,"about_ca_system_score_gemma":0.00005567676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002419312,"about_ca_topic_score_gemma":0.0006110477,"domain_scores_codex":[0.9991494,0.00002306523,0.0001630094,0.0004394842,0.00005923855,0.0001658093],"domain_scores_gemma":[0.9991619,0.0001008315,0.00008515657,0.0005268864,0.00009340723,0.00003183161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004729551,0.0002062168,0.001646656,0.0002174309,0.000293513,0.00000342886,0.000560307,0.001595206,0.1195375,0.5843366,0.003581334,0.2875488],"study_design_scores_gemma":[0.0005483525,0.0007297995,0.003109436,0.00008127472,0.00004811315,0.00003056,0.0001098167,0.8512349,0.1138651,0.01998277,0.009886724,0.0003731733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004237209,0.00002772709,0.993916,0.0004136579,0.00007776127,0.0004402233,0.000005687225,0.00041127,0.0004704596],"genre_scores_gemma":[0.7931086,0.000008556,0.2043678,0.0001541982,0.00007263716,0.0001897434,0.00001405728,0.000008605986,0.002075813],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8496397,"threshold_uncertainty_score":0.361003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01073050233633027,"score_gpt":0.260440038659818,"score_spread":0.2497095363234877,"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."}}