{"id":"W4389077626","doi":"10.23919/cnsm59352.2023.10327854","title":"Analyzing the Quality of Synthetic Adversarial Cyberattacks","year":2023,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Adversarial system; Autoencoder; Computer science; Artificial intelligence; Machine learning; Adversarial machine learning; Generative grammar; Face (sociological concept); Generative adversarial network; Quality (philosophy); Vulnerability (computing); Deep learning; Computer security","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":[0.003114301,0.0007948773,0.0005029066,0.0007841176,0.0002953592,0.0006962668,0.0007305135,0.0009080051,0.0008123194],"category_scores_gemma":[0.0155084,0.0003055988,0.0004777596,0.0003666747,0.001132011,0.001097603,0.0009259295,0.001401962,0.0001379644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001171622,"about_ca_system_score_gemma":0.0005930705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003989966,"about_ca_topic_score_gemma":0.003856215,"domain_scores_codex":[0.9989432,0.000331606,0.00006439949,0.0001778649,0.0003720337,0.0001108958],"domain_scores_gemma":[0.9891338,0.007482371,0.001002881,0.001081532,0.0010168,0.0002827044],"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":[0.00008605931,0.00004143711,0.002608489,0.00004239364,0.00004029958,0.00003917623,0.00002544219,0.9857307,0.002055904,0.001704535,0.0003350875,0.007290376],"study_design_scores_gemma":[0.00000487765,0.00005248796,0.0008476336,0.000008211281,0.000006304405,0.00002772677,0.00001003113,0.9957522,0.002112656,0.00103708,0.0001346407,0.000005980618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7379462,0.0008956349,0.2542719,0.0007367376,0.000156401,0.0001419301,0.0004871041,0.0008874447,0.004476742],"genre_scores_gemma":[0.9848682,0.0001247527,0.0139679,0.00008208486,0.00001133339,0.00002338038,0.0004171998,0.0000406842,0.0004644175],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003989966,"threshold_uncertainty_score":0.01647019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03200784841330049,"score_gpt":0.2985135364186849,"score_spread":0.2665056880053844,"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."}}