{"id":"W4386256163","doi":"10.24908/iqurcp16688","title":"Leveraging Dual-Generative Adversarial Networks for Adversarial Malware Detection via Ensemble Learning","year":2023,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Malware; Adversarial system; Executable; Adversarial machine learning; Artificial intelligence; Machine learning; Robustness (evolution); Scalability; Generator (circuit theory); Computer security; Programming language; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001858323,0.001065103,0.0009208526,0.0007780594,0.0003819981,0.0008151007,0.00145801,0.001137491,0.001208194],"category_scores_gemma":[0.004888615,0.0005047493,0.00072086,0.0003959545,0.001049721,0.001499898,0.002063678,0.002114568,0.0005182435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007162853,"about_ca_system_score_gemma":0.0005371023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001977394,"about_ca_topic_score_gemma":0.002696606,"domain_scores_codex":[0.999116,0.0002942945,0.00003053394,0.0002167477,0.0002286233,0.0001137745],"domain_scores_gemma":[0.9973087,0.001681765,0.00020079,0.0003271031,0.0003837351,0.000097856],"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":[0.00006911357,0.00006735192,0.001453709,0.0000248228,0.00006500835,0.00009632851,0.00004993565,0.9229292,0.004403852,0.008060951,0.001070978,0.06170868],"study_design_scores_gemma":[7.985229e-7,0.000008500537,0.00004107329,0.00000133078,0.000002528627,0.00001050048,0.000001246795,0.9977599,0.0005231413,0.001561885,0.00008687523,0.000002257069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02294846,0.0001932926,0.973765,0.0003054192,0.00004659795,0.00003703616,0.00003650713,0.0008105484,0.001857121],"genre_scores_gemma":[0.8732971,0.0002225251,0.1204822,0.0004281673,0.0001020862,0.00009290862,0.0002059272,0.0001193237,0.005049772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001977394,"threshold_uncertainty_score":0.009827852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08424423758408807,"score_gpt":0.3564750883087938,"score_spread":0.2722308507247057,"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."}}