{"id":"W3210231570","doi":"10.1007/s10922-021-09634-4","title":"Effective and Efficient Hybrid Android Malware Classification Using Pseudo-Label Stacked Auto-Encoder","year":2021,"lang":"en","type":"article","venue":"Journal of Network and Systems Management","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":201,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor; University of New Brunswick","funders":"","keywords":"Computer science; Malware; Android (operating system); Machine learning; Android malware; Artificial intelligence; Encoder; Static analysis; Mobile device; Computer security; Operating system","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":[0.0004553729,0.001288862,0.001063494,0.0011219,0.0005781245,0.0008587983,0.0009390335,0.0007682673,0.001637406],"category_scores_gemma":[0.001265392,0.0003095073,0.0006625152,0.0005189431,0.000271129,0.001440898,0.001179081,0.0009195638,0.001723166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004016458,"about_ca_system_score_gemma":0.001171329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005150892,"about_ca_topic_score_gemma":0.01090618,"domain_scores_codex":[0.9993206,0.00008254137,0.00003490082,0.0001551114,0.0002808978,0.0001258911],"domain_scores_gemma":[0.9992342,0.0002144752,0.00005286101,0.0001151132,0.0003365154,0.00004679833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005598589,0.0003109183,0.003813467,0.0001215569,0.00008874527,0.0002982023,0.00007815217,0.01960824,0.06638242,0.002021513,0.0105353,0.8961816],"study_design_scores_gemma":[0.00001728884,0.0001217275,0.002043682,0.00001779325,0.00006648932,0.000326038,0.00004597532,0.9590987,0.03410704,0.001685705,0.002440185,0.0000294547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2051833,0.003201588,0.7659467,0.0006387153,0.0007578757,0.0001674644,0.001215492,0.01655442,0.006334515],"genre_scores_gemma":[0.709175,0.0008066998,0.2688075,0.0004950453,0.0002305294,0.0001318744,0.003300469,0.0003764688,0.01667649],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005150892,"threshold_uncertainty_score":0.01024181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02156520709240784,"score_gpt":0.2674306129995809,"score_spread":0.245865405907173,"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."}}