{"id":"W4379383024","doi":"10.32920/23296247","title":"An Automated Approach for Privacy Leakage Identification in IoT Apps","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Taint checking; Computer science; False positive paradox; Path (computing); Context (archaeology); Precision and recall; Information sensitivity; Internet of Things; Information leakage; Mobile apps; Static analysis; True positive rate; Data mining; Embedded system; Machine learning; Artificial intelligence; Computer security; Operating system; World Wide Web; Software; 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":[0.001830059,0.001494223,0.000949139,0.006982133,0.001095986,0.002397459,0.00157504,0.001318906,0.001285129],"category_scores_gemma":[0.009554191,0.0008475007,0.002004849,0.00282597,0.0009270258,0.00311071,0.002580361,0.002046149,0.001676382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001030681,"about_ca_system_score_gemma":0.002253795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004110294,"about_ca_topic_score_gemma":0.006073306,"domain_scores_codex":[0.9944332,0.000833301,0.0004404931,0.001330851,0.002627004,0.0003351555],"domain_scores_gemma":[0.9919364,0.002914785,0.001346746,0.002125965,0.001555384,0.0001206136],"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":[0.0003458982,0.0003729049,0.05395238,0.0008964966,0.0003993603,0.001184204,0.001272899,0.01999496,0.05574127,0.01068567,0.02972218,0.8254317],"study_design_scores_gemma":[0.00007122755,0.0002563347,0.02408853,0.000407081,0.0003583762,0.002672858,0.0006285464,0.7369104,0.1357494,0.04599587,0.05267598,0.0001853322],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1447375,0.003819023,0.7458756,0.002006624,0.000316103,0.0007313587,0.005904762,0.08938772,0.007221149],"genre_scores_gemma":[0.5673392,0.001035327,0.4160425,0.000733531,0.0001967385,0.0003295932,0.00798366,0.001721305,0.004618202],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006982133,"threshold_uncertainty_score":0.009678364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05311663460667621,"score_gpt":0.3660797978074911,"score_spread":0.3129631632008149,"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."}}