{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004895296,0.0001578491,0.0002534804,0.0007964785,0.00006329687,0.0001954536,0.000667333,0.0001370736,0.00002958258],"category_scores_gemma":[0.0001697309,0.000134985,0.0001732577,0.0003946142,0.0001043765,0.001474611,0.0004582817,0.0006825348,0.000003013035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002473398,"about_ca_system_score_gemma":0.00004877288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003311373,"about_ca_topic_score_gemma":0.00001274654,"domain_scores_codex":[0.9985387,0.00009501437,0.0005973928,0.0002450987,0.0003936868,0.000130077],"domain_scores_gemma":[0.9985388,0.0001970492,0.0005580251,0.0001549848,0.0005265928,0.00002453214],"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":[0.00008227589,0.00008665605,0.0001141902,0.00005601955,0.000217597,0.00005142865,0.0003955571,0.0007137408,0.09666736,0.0009077899,0.00003703569,0.9006703],"study_design_scores_gemma":[0.0003172978,0.002298672,0.00008353582,0.001915209,0.00004614928,0.00171105,0.0007368653,0.1884941,0.7588058,0.01063443,0.03469148,0.0002654073],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6702747,0.0117982,0.3149925,0.0002107738,0.001776063,0.0003049064,0.000005307264,0.000489946,0.00014761],"genre_scores_gemma":[0.985315,0.00405032,0.01044157,0.00001369544,0.0001064113,0.00002506417,9.64755e-7,0.00001211342,0.00003491418],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9004049,"threshold_uncertainty_score":0.5504529,"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."}}