{"id":"W4401371280","doi":"10.1016/j.iot.2024.101320","title":"The revolution and vision of explainable AI for Android malware detection and protection","year":2024,"lang":"en","type":"article","venue":"Internet of Things","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Key Scientific Instrument and Equipment Development Projects of China; Guangdong Provincial Pearl River Talents Program; Distinguished Young Scholar Foundation of Hunan Province; Leading Talents Program of Guangdong Province; Guangzhou Science, Technology and Innovation Commission; National Aerospace Science Foundation of China; Department of Natural Resources of Guangdong Province","keywords":"Malware; Android (operating system); Computer science; Computer security; Artificial intelligence; Machine learning; Denial-of-service attack; Mobile malware; Botnet; Internet privacy; The Internet; World Wide Web; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003840114,0.00007186663,0.00008966636,0.0001071379,0.00007228334,0.00008959462,0.0001370725,0.00005518759,4.968691e-7],"category_scores_gemma":[0.0001054009,0.00005494083,0.00003309971,0.0001367342,0.00006481842,0.0008625581,0.0001262923,0.0001025293,2.782751e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003911015,"about_ca_system_score_gemma":0.00000945543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001173463,"about_ca_topic_score_gemma":0.00001062288,"domain_scores_codex":[0.999374,0.00002477082,0.0001899277,0.0002170185,0.0001036819,0.00009055883],"domain_scores_gemma":[0.9995366,0.00009049758,0.000092391,0.0001478009,0.0001142107,0.0000184489],"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":[0.0001106208,0.00001369085,0.00001120624,0.0005603808,0.00002009623,0.000001130053,0.001443441,0.000007720297,0.1456769,0.01269821,0.000292235,0.8391644],"study_design_scores_gemma":[0.0001352629,0.0009920024,0.0001356471,0.0003546097,0.000008282721,0.00007417902,0.00005959783,0.262968,0.6659072,0.05524906,0.01402083,0.00009530823],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02387092,0.0008524315,0.974003,0.0004470029,0.0002230364,0.0003776209,8.468455e-7,0.0001860662,0.00003912358],"genre_scores_gemma":[0.9842337,0.00008663497,0.01527147,0.00002217268,0.00001593593,0.0000713725,2.035374e-7,0.000007380713,0.0002910796],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9603629,"threshold_uncertainty_score":0.2240422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007392074920776553,"score_gpt":0.2703693446864181,"score_spread":0.2629772697656416,"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."}}