{"id":"W1696275162","doi":"","title":"HIDS:DC-ADT : An Effective Hybrid Intrusion Detection System based on Data Correlation and Adaboost based Decision Tree classifier","year":2012,"lang":"en","type":"article","venue":"Journal of academic and applied studies","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Intrusion detection system; Computer science; AdaBoost; Anomaly detection; Decision tree; Data mining; Anomaly-based intrusion detection system; Pattern recognition (psychology); Artificial intelligence; Classifier (UML); Correlation; Misuse detection; Network security; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00154045,0.0001985994,0.0003521253,0.0002531939,0.0004264336,0.00005822887,0.0003173308,0.000156507,0.000001192241],"category_scores_gemma":[0.0001357803,0.0001476496,0.00003656407,0.0002299265,0.00008485244,0.001027531,0.000241455,0.0007128667,0.000002622886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001047152,"about_ca_system_score_gemma":0.00002319379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001490707,"about_ca_topic_score_gemma":0.000002106416,"domain_scores_codex":[0.9983716,0.0001697741,0.000479271,0.000315283,0.0004371845,0.0002268943],"domain_scores_gemma":[0.998246,0.0007027416,0.0004962541,0.000293198,0.00009304969,0.0001687197],"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":[0.000712515,0.00007748298,0.001262721,0.00008409174,0.00005223044,0.000003988198,0.000648679,0.001138877,0.005536411,0.001087606,0.0006426743,0.9887527],"study_design_scores_gemma":[0.003692477,0.001577084,0.04214227,0.001151203,0.000216366,0.0002405175,0.001315319,0.9205398,0.02307286,0.001260378,0.004226063,0.0005656391],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.490555,0.002279619,0.5055209,0.0001842502,0.001005785,0.0002682888,0.00000354827,0.00004681624,0.0001358538],"genre_scores_gemma":[0.9950427,0.0005313561,0.003531995,0.0002539337,0.0006168427,0.000008188428,0.000002216156,0.00000995384,0.000002743546],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9881871,"threshold_uncertainty_score":0.6020974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03289712513798451,"score_gpt":0.2853646790845276,"score_spread":0.2524675539465431,"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."}}