{"id":"W2132968752","doi":"10.1109/cec.2003.1299940","title":"Detecting new forms of network intrusion using genetic programming","year":2003,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Constant false alarm rate; Crossover; False positive rate; Genetic programming; Intrusion detection system; Computer science; False alarm; Artificial intelligence; Network security; Data mining; Genetic algorithm; Mutation; Anomaly-based intrusion detection system; Machine learning; Computer security","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":[0.0003427318,0.0001143743,0.0001478884,0.00007372045,0.0002070469,0.00008420521,0.0002824518,0.00007913192,0.00005225105],"category_scores_gemma":[0.00005441282,0.00009892075,0.00006882648,0.0007991458,0.00002049665,0.0003501957,0.0001384639,0.0001290376,0.000006394787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003319058,"about_ca_system_score_gemma":0.00007238851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000819132,"about_ca_topic_score_gemma":0.00004313973,"domain_scores_codex":[0.9988115,0.00006372741,0.0003172085,0.0002599625,0.0002083005,0.0003392297],"domain_scores_gemma":[0.9993237,0.00004857012,0.0001514031,0.0003216569,0.00005524393,0.00009943848],"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":[0.000007014155,0.00003059404,0.0008571065,0.00001834565,0.00001311596,0.000003485065,0.0003745588,0.01784683,0.002589775,0.04238781,0.0001085955,0.9357628],"study_design_scores_gemma":[0.0008424163,0.0005595592,0.0008420948,0.0001968868,0.00002541447,0.0002517762,0.0001060479,0.811852,0.0787847,0.07806273,0.02781697,0.0006593853],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2207856,0.0002090502,0.7772425,0.00001629819,0.0004205928,0.0001383177,1.978251e-8,0.0001035834,0.001084068],"genre_scores_gemma":[0.5821059,0.0000130117,0.4176661,0.00005657579,0.0001088547,0.000001449827,8.04833e-8,0.000006329242,0.00004177514],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9351034,"threshold_uncertainty_score":0.4033871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02017044237801582,"score_gpt":0.2418406085281896,"score_spread":0.2216701661501737,"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."}}