{"id":"W4200093156","doi":"10.33423/jabe.v23i6.4648","title":"Protecting Accounting Information Systems Using Machine Learning Based Intrusion Detection","year":2021,"lang":"en","type":"article","venue":"Journal of Applied Business and Economics","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Support vector machine; Decision tree; Data mining; Anomaly detection; Artificial intelligence; Intrusion detection system; Machine learning; Constant false alarm rate; Feature selection; Classifier (UML); k-nearest neighbors algorithm; Traffic classification; Pattern recognition (psychology); The Internet","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001381496,0.0005144557,0.0007449622,0.002392517,0.000452313,0.001943469,0.0006257929,0.0005032922,0.0005577347],"category_scores_gemma":[0.004356196,0.0002325741,0.0004301608,0.001336272,0.0003996916,0.002399228,0.000726392,0.0006821862,0.0003323779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006470365,"about_ca_system_score_gemma":0.000632613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009079878,"about_ca_topic_score_gemma":0.0009700689,"domain_scores_codex":[0.9980697,0.000583433,0.0001848665,0.0002683007,0.0007479563,0.0001458317],"domain_scores_gemma":[0.9975126,0.0008282068,0.0006049356,0.0004476306,0.0005513951,0.00005529293],"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":[0.0003898657,0.000607084,0.03761578,0.0003338214,0.0002869512,0.000427517,0.0003446391,0.08610566,0.02681132,0.009785956,0.004460768,0.8328307],"study_design_scores_gemma":[0.0000150948,0.0002702746,0.01103027,0.00006439021,0.00006976917,0.0005589095,0.0001487996,0.9471061,0.02954939,0.006610485,0.004528899,0.00004756601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2907786,0.001794149,0.6840711,0.001216538,0.0002241966,0.0004579145,0.000364272,0.009550373,0.01154293],"genre_scores_gemma":[0.9370394,0.0003138381,0.06146268,0.00008408606,0.00004894566,0.00005808783,0.000156366,0.00001707243,0.0008195968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002392517,"threshold_uncertainty_score":0.007306159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009876157397984113,"score_gpt":0.184995858199319,"score_spread":0.1751197008013349,"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."}}