{"id":"W4367046386","doi":"10.1016/j.jisa.2023.103486","title":"AIM: An Android Interpretable Malware detector based on application class modeling","year":2023,"lang":"en","type":"article","venue":"Journal of Information Security and Applications","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Malware; Computer science; Interpretability; Android (operating system); Machine learning; Android malware; Cryptovirology; Popularity; Mobile malware; Artificial intelligence; Class (philosophy); Classifier (UML); Computer security; Pace; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002432259,0.001456634,0.0007616223,0.001211373,0.0003268038,0.0009753064,0.0009307261,0.0007575065,0.002671672],"category_scores_gemma":[0.001056755,0.0003667213,0.0007306172,0.0002681165,0.0002241957,0.001104529,0.000774278,0.0007756264,0.001937566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003335461,"about_ca_system_score_gemma":0.00051658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001651435,"about_ca_topic_score_gemma":0.00263954,"domain_scores_codex":[0.9996538,0.0000251331,0.00001880814,0.00009759818,0.0001591976,0.00004542521],"domain_scores_gemma":[0.9995748,0.00009982296,0.00004881909,0.0001110083,0.0001380093,0.00002746778],"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":[0.001636873,0.0005801531,0.02216183,0.0006858239,0.0003000257,0.001685681,0.0003118245,0.01599229,0.2167192,0.006426142,0.04019362,0.6933066],"study_design_scores_gemma":[0.00005985909,0.0003656889,0.01010155,0.00005841828,0.0001860638,0.001521971,0.00007605256,0.8290507,0.1353406,0.00374147,0.0194135,0.00008431444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1556386,0.001367504,0.6856453,0.0005356399,0.0004817967,0.0006223625,0.002180997,0.1431844,0.01034335],"genre_scores_gemma":[0.7660922,0.0004178764,0.2129766,0.000573308,0.0001245522,0.000276705,0.002482593,0.001654863,0.01540131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002671672,"threshold_uncertainty_score":0.008937597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00841803079208307,"score_gpt":0.2592795519376299,"score_spread":0.2508615211455468,"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."}}