{"id":"W3037028657","doi":"10.1007/s12652-020-02243-0","title":"TriDroid: a triage and classification framework for fast detection of mobile threats in android markets","year":2020,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Deanship of Scientific Research, King Saud University","keywords":"Computer science; Malware; Android (operating system); Triage; Botnet; Computer security; Queue; Queueing theory; Mobile device; Machine learning; Artificial intelligence; Operating system; Computer network; The Internet","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":[0.0008231006,0.001360021,0.00123728,0.003395325,0.0006908897,0.001301894,0.001637636,0.001267554,0.003867735],"category_scores_gemma":[0.00275927,0.0004843428,0.0007992827,0.0007846275,0.0002990861,0.002337127,0.002141332,0.001211066,0.002905185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005074273,"about_ca_system_score_gemma":0.0009390747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005549038,"about_ca_topic_score_gemma":0.009265294,"domain_scores_codex":[0.9992499,0.00007285146,0.00006632326,0.0001583083,0.0003454321,0.000107166],"domain_scores_gemma":[0.9989824,0.000332943,0.0001530191,0.0001451771,0.0002539146,0.00013255],"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":[0.001440836,0.0007014413,0.02654112,0.0006512473,0.000336329,0.0009214659,0.0005817343,0.02057662,0.04661453,0.005746201,0.08853552,0.807353],"study_design_scores_gemma":[0.0001043255,0.0004287127,0.009301821,0.0001169811,0.00009417747,0.001009988,0.000235264,0.9213249,0.02934766,0.007614149,0.03026825,0.0001538019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08197019,0.00273778,0.7707958,0.0008124041,0.0006427768,0.0009592474,0.005813152,0.1308831,0.005385591],"genre_scores_gemma":[0.5232538,0.0008901104,0.4549579,0.0007242533,0.0002739816,0.0006500035,0.00651237,0.001086054,0.01165146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005549038,"threshold_uncertainty_score":0.01293892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05495597856918051,"score_gpt":0.3218945973613963,"score_spread":0.2669386187922158,"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."}}