{"id":"W1737793783","doi":"10.3233/ida-2011-0496","title":"Robust learning intrusion detection for attacks on wireless networks","year":2011,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"National Institute for Materials Science; Natural Sciences and Engineering Research Council of Canada; Research Nova Scotia; Dalhousie University","keywords":"Computer science; Intrusion detection system; Wireless network; Computer network; Computer security; Wireless; Artificial intelligence; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000869323,0.0002077211,0.0002998172,0.0004988486,0.0004297814,0.0001817397,0.001587711,0.0001575793,0.0001396632],"category_scores_gemma":[0.00009441443,0.0001934445,0.0002218606,0.001852434,0.00003808587,0.0006884533,0.0006364572,0.0003550734,0.00006460896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006816301,"about_ca_system_score_gemma":0.00001438484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002127532,"about_ca_topic_score_gemma":0.0006125256,"domain_scores_codex":[0.9978849,0.0001554399,0.0004237749,0.0008930693,0.0002880196,0.0003547602],"domain_scores_gemma":[0.9978087,0.0001778461,0.0002317828,0.001522995,0.0001405423,0.0001181166],"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":[0.0001341655,0.0001677146,0.000881115,0.00001030492,0.000637162,0.000005539141,0.000462565,0.1217348,0.00007668355,0.002526625,0.0006384056,0.872725],"study_design_scores_gemma":[0.00008750903,0.0002572935,0.0002584971,0.00001620601,0.0002785423,0.000001923597,0.00003940583,0.9840972,0.004894938,0.0004612902,0.009377915,0.0002293248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01167585,0.0001087304,0.9868529,0.00004821808,0.0006342417,0.0001999902,0.000007983042,0.0002130105,0.0002590378],"genre_scores_gemma":[0.9902914,0.0004862474,0.008410247,0.0001730081,0.0002779862,0.00002753197,0.0001996177,0.00001616487,0.0001178061],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9786155,"threshold_uncertainty_score":0.7888439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09837774240311724,"score_gpt":0.2796497730222798,"score_spread":0.1812720306191625,"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."}}