{"id":"W4379390788","doi":"10.32920/23296247.v1","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); Precision and recall; Context (archaeology); Information sensitivity; Information leakage; Internet of Things; Data mining; Code (set theory); True positive rate; Embedded system; Information retrieval; Machine learning; Artificial intelligence; Computer security; Operating system; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006461995,0.0002695236,0.0003066879,0.0006211108,0.00007306307,0.000369169,0.001939683,0.0003339073,0.000002708529],"category_scores_gemma":[0.0001430456,0.0002879223,0.00009766292,0.0005755798,0.00002569555,0.0005511949,0.001265764,0.0003864102,0.00001689316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002215043,"about_ca_system_score_gemma":0.0001100477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001538785,"about_ca_topic_score_gemma":0.00003449014,"domain_scores_codex":[0.9977303,0.00008784111,0.0004855326,0.001113994,0.0002609533,0.0003214082],"domain_scores_gemma":[0.9978865,0.00008191434,0.0002448862,0.001558708,0.0001554918,0.00007247501],"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":[0.0001150858,0.002433307,0.000951294,0.003982591,0.0002003837,0.00005765062,0.007145626,0.3147325,0.02549514,0.1664567,0.01990591,0.4585238],"study_design_scores_gemma":[0.0001657348,0.00005988089,0.001172999,0.00004596289,0.000003527867,0.000003387635,0.00003598766,0.9334584,0.01750444,0.04696362,0.0002413831,0.0003446283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00195568,0.00001560181,0.9792066,0.0001456641,0.0004479148,0.001738495,0.000007067798,0.01614549,0.0003374812],"genre_scores_gemma":[0.2338656,0.00001277217,0.7639185,0.00006192536,0.00006182654,0.001468482,0.0001072104,0.00004343257,0.0004602819],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.618726,"threshold_uncertainty_score":0.9999573,"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."}}