{"id":"W2858044321","doi":"10.1109/noms.2018.8406212","title":"Evaluation of machine learning techniques for network intrusion detection","year":2018,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Anomaly detection; Computer science; Intrusion detection system; Data mining; Anomaly-based intrusion detection system; Metric (unit); Machine learning; Artificial intelligence; Precision and recall; Entropy (arrow of time); Network security; Signature (topology); Set (abstract data type); Engineering; Computer security; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01042756,0.001564476,0.001270511,0.005257702,0.0005101215,0.00127256,0.001172482,0.001176914,0.0006263568],"category_scores_gemma":[0.03302415,0.0002026882,0.0009066823,0.003267052,0.0003650786,0.002002229,0.0007964146,0.0008513908,0.0003365571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001213272,"about_ca_system_score_gemma":0.0007646285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003586437,"about_ca_topic_score_gemma":0.001918193,"domain_scores_codex":[0.9871151,0.004858556,0.001308027,0.0009841935,0.005347314,0.0003867399],"domain_scores_gemma":[0.9549879,0.03367325,0.001833665,0.001477383,0.007688119,0.0003397573],"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":[0.00129528,0.0009748153,0.03088932,0.0009483278,0.000908893,0.000185938,0.0002026358,0.2740233,0.005102455,0.002117723,0.003922422,0.6794288],"study_design_scores_gemma":[0.00004111949,0.000888599,0.01159724,0.00006403657,0.0001186313,0.0001711529,0.0001068699,0.9792548,0.005538358,0.0008116699,0.001375752,0.00003176592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6898517,0.02145216,0.2701588,0.001024119,0.0007141308,0.0005918495,0.001394533,0.00386354,0.01094915],"genre_scores_gemma":[0.8813783,0.002169732,0.1137367,0.00007216735,0.0001116104,0.0001695031,0.001274871,0.00007518335,0.001012064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01042756,"threshold_uncertainty_score":0.05514687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02537583609063089,"score_gpt":0.2844474219947883,"score_spread":0.2590715859041575,"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."}}