{"id":"W2340512350","doi":"","title":"Intrusion Detection in Computer Networks Using Combination of Machine Learning Techniques","year":2015,"lang":"en","type":"article","venue":"Journal of academic and applied studies","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"C4.5 algorithm; Computer science; Intrusion detection system; Feature selection; Artificial intelligence; Machine learning; Decision tree; AdaBoost; Naive Bayes classifier; Data mining; Pattern recognition (psychology); Statistical classification; Support vector machine","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":[0.0009344883,0.000672481,0.001464364,0.003025275,0.0003824444,0.001124297,0.0005686856,0.0006021254,0.0006830462],"category_scores_gemma":[0.001797354,0.000296362,0.0008672997,0.002089272,0.0002267025,0.001271831,0.0005820009,0.0005776971,0.0003997095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003114203,"about_ca_system_score_gemma":0.0003050001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00065824,"about_ca_topic_score_gemma":0.001016535,"domain_scores_codex":[0.9981028,0.0004455542,0.0001849063,0.0002493036,0.0009070472,0.0001104609],"domain_scores_gemma":[0.9991378,0.0003512025,0.0001248218,0.0001037784,0.0002575194,0.00002491537],"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":[0.0001910488,0.0002840168,0.01323971,0.0004869505,0.0005197759,0.0003425109,0.000128173,0.04985342,0.03052009,0.001883165,0.00263137,0.8999198],"study_design_scores_gemma":[0.00003204715,0.0007724941,0.01984803,0.0001372572,0.0002719474,0.001571469,0.0001472887,0.9251291,0.0345563,0.005953053,0.01147996,0.0001010383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1324039,0.005820313,0.8499029,0.0004205076,0.0003254518,0.0003544585,0.0002093096,0.003386386,0.007176721],"genre_scores_gemma":[0.7063984,0.002930866,0.2871033,0.0001456635,0.0001527397,0.0002065074,0.0003531766,0.00006209568,0.002647269],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003025275,"threshold_uncertainty_score":0.00494206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03320851391119029,"score_gpt":0.2863153660887915,"score_spread":0.2531068521776013,"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."}}