{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002979277,0.00007768285,0.00009978207,0.00008950781,0.0002649172,0.00003647987,0.0002025354,0.0000811435,0.00007857374],"category_scores_gemma":[0.0001763118,0.00006917288,0.00005077825,0.0004147247,0.0000365819,0.0003185455,0.0001080056,0.00009110908,0.000007714809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005180153,"about_ca_system_score_gemma":0.00003071862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005192076,"about_ca_topic_score_gemma":0.0002314376,"domain_scores_codex":[0.9988247,0.0001900689,0.0002177967,0.0002190182,0.0003943249,0.0001540672],"domain_scores_gemma":[0.9987661,0.00006916469,0.0001412038,0.0002081959,0.0007872435,0.0000281286],"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":[0.00002230262,0.00001650302,0.00002711036,0.000005075159,0.000007633117,2.308978e-8,0.000105575,0.0002804478,0.011023,0.004420048,0.0001992356,0.983893],"study_design_scores_gemma":[0.0001509687,0.0005789941,0.00008692603,0.00001559757,0.00001429814,0.000002774426,0.000003346291,0.7144102,0.253595,0.01900025,0.01207506,0.00006663679],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01839523,0.00008948695,0.9775788,0.0000845388,0.0005530525,0.0003913813,2.233763e-7,0.0002639147,0.002643344],"genre_scores_gemma":[0.9421516,0.00002247575,0.05708439,0.00006664963,0.0005607097,0.00004453513,0.000001665695,0.000006063659,0.0000619282],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9838264,"threshold_uncertainty_score":0.2820788,"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."}}