{"id":"W4405522338","doi":"10.1109/iisa62523.2024.10786687","title":"Deep Learning-Based Anomaly Detection in 5G Cellular Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Telecommunications and Broadcasting Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; École de Technologie Supérieure","funders":"","keywords":"Anomaly detection; Computer science; Deep learning; Artificial intelligence; Cellular network; Anomaly (physics); Computer network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008404696,0.000575286,0.0005485888,0.0006451774,0.000280832,0.0006010987,0.0008454553,0.0007603988,0.0003514604],"category_scores_gemma":[0.002681345,0.0002233793,0.0002764909,0.0006772556,0.0004674918,0.0009749936,0.0007040855,0.001194651,0.0001039974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028192,"about_ca_system_score_gemma":0.0006092373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009819326,"about_ca_topic_score_gemma":0.007173319,"domain_scores_codex":[0.999556,0.00009754323,0.00002589733,0.00009240951,0.000124359,0.0001037212],"domain_scores_gemma":[0.9990808,0.0004740643,0.0001261963,0.00006776939,0.0002075562,0.00004358736],"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":[0.0002184949,0.00009346976,0.008304329,0.00004245428,0.00005390848,0.0001758351,0.00007187863,0.8355979,0.004484749,0.003564666,0.001302961,0.1460894],"study_design_scores_gemma":[8.097762e-7,0.000007662563,0.0002399807,0.000001486501,0.000001935564,0.000009761418,0.000004199727,0.9982786,0.0005223987,0.0008547812,0.00007695772,0.000001440611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.316824,0.001087499,0.6770791,0.0008769703,0.0001243784,0.00003612379,0.0002523191,0.001550388,0.002169121],"genre_scores_gemma":[0.9816834,0.0001599257,0.01720239,0.00007402618,0.00002046886,0.00001040265,0.0001494892,0.00001438338,0.0006855917],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009819326,"threshold_uncertainty_score":0.01952434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006900664786662278,"score_gpt":0.1941223230750715,"score_spread":0.1872216582884092,"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."}}