{"id":"W4389474068","doi":"10.1016/j.isatra.2023.12.005","title":"Anomaly detection using deep convolutional generative adversarial networks in the internet of things","year":2023,"lang":"en","type":"article","venue":"ISA Transactions","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Overfitting; Hyperparameter; Artificial intelligence; Machine learning; Regularization (linguistics); Anomaly detection; Deep learning; Internet of Things; Convolutional neural network; Adversarial system; Data mining; Computer security; Artificial neural network","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":[0.0006726329,0.0004661708,0.000624097,0.0005490077,0.0002972412,0.000605061,0.001018839,0.0007160518,0.0007029839],"category_scores_gemma":[0.001613788,0.0003835594,0.0005768049,0.000684824,0.0005725747,0.0009355569,0.0009244331,0.001360171,0.000237252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007345105,"about_ca_system_score_gemma":0.0004808653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006433443,"about_ca_topic_score_gemma":0.00689348,"domain_scores_codex":[0.9996635,0.00007481835,0.00001177001,0.00009792927,0.00009294625,0.00005902847],"domain_scores_gemma":[0.9994044,0.0003173278,0.00005448268,0.0000914476,0.0001043074,0.00002792753],"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":[0.0001056988,0.00006024301,0.002536888,0.00003879372,0.00007611466,0.0001264719,0.00004149823,0.8880853,0.003012201,0.01868319,0.002256093,0.08497745],"study_design_scores_gemma":[7.245901e-7,0.000004872629,0.0001337908,0.000001306836,0.000002719828,0.000009170302,0.000001965006,0.9958805,0.0003963229,0.003415575,0.0001512051,0.000001813203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08443136,0.000631763,0.9098908,0.0006710204,0.0001895718,0.00002375176,0.0001923004,0.001296382,0.002673042],"genre_scores_gemma":[0.9376384,0.0002992809,0.05800689,0.0001497764,0.00006602565,0.00002150745,0.0003833812,0.00008019723,0.003354601],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006433443,"threshold_uncertainty_score":0.01279199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02116092301779926,"score_gpt":0.2394036031537797,"score_spread":0.2182426801359804,"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."}}