{"id":"W4398222278","doi":"10.3991/ijim.v18i10.46485","title":"Overview of Mobile Attack Detection and Prevention Techniques Using Machine Learning","year":2024,"lang":"en","type":"article","venue":"International Journal of Interactive Mobile Technologies (iJIM)","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Computer security","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":[0.001432289,0.001053346,0.0007834003,0.004173269,0.0005784582,0.002086529,0.001520626,0.001546432,0.002696061],"category_scores_gemma":[0.002125785,0.0006777783,0.001137911,0.002980785,0.0005316602,0.002987659,0.0008979578,0.001794623,0.002518394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008391265,"about_ca_system_score_gemma":0.0008640054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00154336,"about_ca_topic_score_gemma":0.001149005,"domain_scores_codex":[0.998979,0.0001967938,0.0001053128,0.0002169154,0.0004335135,0.00006857289],"domain_scores_gemma":[0.9987763,0.0005555242,0.0001173376,0.0001366729,0.000363054,0.00005109993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005038152,0.0001553625,0.001862223,0.001479853,0.0001024628,0.0001469157,0.0001415555,0.01568656,0.004361372,0.02050159,0.009000273,0.9465114],"study_design_scores_gemma":[0.0000320185,0.0007526832,0.00720672,0.002051624,0.0003029715,0.00263726,0.0002781215,0.2941635,0.01797759,0.06754898,0.606784,0.0002645258],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.006863715,0.1642307,0.7928124,0.003144724,0.000759965,0.0004511756,0.0003690513,0.002033226,0.02933502],"genre_scores_gemma":[0.1637308,0.2282918,0.580238,0.002054139,0.003009124,0.0006188254,0.001955357,0.0003104725,0.01979148],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.004173269,"threshold_uncertainty_score":0.009019256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03308803779173196,"score_gpt":0.3473560224785369,"score_spread":0.3142679846868049,"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."}}