{"id":"W2594215738","doi":"10.1109/malware.2016.7888739","title":"DySign: dynamic fingerprinting for the automatic detection of android malware","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Malware; Android (operating system); Computer science; Emulation; Static analysis; Android malware; Cryptovirology; Computer security; Mobile malware; System call; Operating system; Programming language","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":[0.0004422313,0.0009133705,0.0004716446,0.002302708,0.000361204,0.000550646,0.0005870565,0.0007346254,0.002370238],"category_scores_gemma":[0.002528518,0.0003136434,0.0004675825,0.0005249145,0.0004771126,0.001128741,0.0008950896,0.000630123,0.001842915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003594725,"about_ca_system_score_gemma":0.0004549725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001184568,"about_ca_topic_score_gemma":0.001841073,"domain_scores_codex":[0.9993739,0.00006052646,0.00004464934,0.0001555039,0.0003019188,0.00006359231],"domain_scores_gemma":[0.9992259,0.0003133249,0.0001458,0.0001396825,0.0001427041,0.00003267733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006547868,0.0001392106,0.007183828,0.0003955641,0.00007431259,0.0008365782,0.0003921908,0.006429567,0.1704464,0.003371045,0.01969695,0.7903795],"study_design_scores_gemma":[0.0001129147,0.0005890964,0.0191963,0.0001178146,0.000103192,0.004324304,0.0002719896,0.6229476,0.2892649,0.01028434,0.05256645,0.0002210136],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08948293,0.001857581,0.793752,0.0003556443,0.0002546496,0.0004259855,0.003222889,0.1048881,0.005760328],"genre_scores_gemma":[0.5416919,0.0007413609,0.4435926,0.0004027026,0.0001288435,0.000401394,0.004487654,0.00184509,0.006708406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002370238,"threshold_uncertainty_score":0.007929265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00849693374009608,"score_gpt":0.2527077575380793,"score_spread":0.2442108237979833,"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."}}