{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009652764,0.00008618076,0.000261985,0.0002146839,0.00007894955,0.0000140213,0.0001340307,0.000111463,2.047362e-7],"category_scores_gemma":[0.00003810826,0.00007023491,0.00002671794,0.0003047717,0.00005444258,0.0002875371,0.0001909285,0.0006322386,9.171001e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005337365,"about_ca_system_score_gemma":0.00001479074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006068533,"about_ca_topic_score_gemma":0.000002377255,"domain_scores_codex":[0.9990767,0.00007437109,0.000446627,0.0001033179,0.0001951746,0.0001037704],"domain_scores_gemma":[0.9992388,0.00008000613,0.0004720635,0.0000469498,0.0001205284,0.00004158938],"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":[0.0002273493,0.00007241933,0.002147116,0.00005450164,0.00007092238,0.000005520786,0.005532385,0.03323167,0.02500501,0.003546889,0.0001001429,0.9300061],"study_design_scores_gemma":[0.001242095,0.0008151137,0.001219432,0.0003446797,0.00002735976,0.0001539318,0.000552666,0.9344684,0.04745384,0.01291502,0.0006026268,0.0002048603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5028688,0.00263565,0.4940562,0.0001077054,0.0002210444,0.00006754883,4.651852e-8,0.0000171176,0.00002588707],"genre_scores_gemma":[0.9860838,0.002656833,0.01100374,0.00005184381,0.0001974697,0.000001083575,1.028317e-7,0.000003696784,0.000001429545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9298012,"threshold_uncertainty_score":0.2864097,"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."}}