{"id":"W4402796770","doi":"10.1007/978-3-031-72356-8_30","title":"Ch4os: Discretized Generative Adversarial Network for Functionality-Preserving Evasive Modification on Malware","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Adversarial system; Malware; Generative adversarial network; Generative grammar; Discretization; Theoretical computer science; Artificial intelligence; Computer security; Distributed computing; Computer network; Deep learning; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008794517,0.0006313538,0.0005415042,0.000673548,0.0004974358,0.0005973246,0.00247098,0.0004081571,0.00002259434],"category_scores_gemma":[0.0002505315,0.0005842783,0.0002552728,0.0007333142,0.0003994233,0.0007607479,0.001159657,0.0008980226,0.00003434423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007635208,"about_ca_system_score_gemma":0.0004087916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001279975,"about_ca_topic_score_gemma":0.0000526063,"domain_scores_codex":[0.9955035,0.00004959209,0.0006008995,0.00220464,0.0009786534,0.000662677],"domain_scores_gemma":[0.9964845,0.0009707427,0.0003666189,0.001501356,0.0005313877,0.000145348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001014037,0.00002871611,0.000002571701,0.0001033249,0.00004740832,0.0000272159,0.0004758035,0.3800107,0.0002712745,0.3612428,0.000557375,0.2571315],"study_design_scores_gemma":[0.0002336403,0.0003023542,0.00000961436,0.000420378,0.00001412511,0.00001454385,2.626054e-7,0.3940767,0.002560065,0.5942679,0.007590011,0.0005104069],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000005990673,0.0003458802,0.989467,0.001763262,0.004605548,0.001374306,0.00004930748,0.0007087131,0.001679956],"genre_scores_gemma":[0.0133413,0.00005043599,0.9795948,0.001549951,0.003200705,0.0003005781,0.00006525707,0.00008560837,0.001811313],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2566211,"threshold_uncertainty_score":0.9996608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02888085359486251,"score_gpt":0.2896225068197463,"score_spread":0.2607416532248838,"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."}}