{"id":"W4416961926","doi":"10.1109/pst65910.2025.11268833","title":"Using Counterfactuals for Explainable Android Malware Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"Counterfactual thinking; Malware; Counterfactual conditional; Android (operating system); Android malware; GRASP; Static analysis; Mobile device","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":[0.03461035,0.002096993,0.001126056,0.006375029,0.00181946,0.005013878,0.003057601,0.003216682,0.00516543],"category_scores_gemma":[0.2364446,0.001060215,0.00369636,0.002365632,0.006662996,0.008887528,0.004441811,0.004092388,0.0003079321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003784335,"about_ca_system_score_gemma":0.003064302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005935085,"about_ca_topic_score_gemma":0.004634456,"domain_scores_codex":[0.9743212,0.01723688,0.001545145,0.002782567,0.003475623,0.0006386936],"domain_scores_gemma":[0.5998275,0.3514222,0.01890639,0.02228883,0.006425747,0.001129354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004231244,0.0002394762,0.0155974,0.0004323912,0.0003677781,0.00129185,0.001845829,0.1992347,0.001159399,0.7308206,0.00143417,0.04715334],"study_design_scores_gemma":[0.00007379603,0.00006942102,0.001309654,0.0001600828,0.00009583335,0.0002575542,0.0001799635,0.5502759,0.001827293,0.443101,0.002575856,0.00007362661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07519795,0.0005128388,0.9173831,0.002315612,0.0001180595,0.0002969539,0.0004803613,0.0005839207,0.003111332],"genre_scores_gemma":[0.6972576,0.0003212784,0.2998493,0.0003952248,0.0001588226,0.000554678,0.0007279784,0.0001259186,0.0006092501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03461035,"threshold_uncertainty_score":0.1830392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04103805696767971,"score_gpt":0.3439023649920591,"score_spread":0.3028643080243794,"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."}}