{"id":"W2117631199","doi":"10.1007/3-540-45110-2_125","title":"A Linear Genetic Programming Approach to Intrusion Detection","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Benchmark (surveying); Computer science; Genetic programming; Intrusion detection system; Selection (genetic algorithm); Decoding methods; Class (philosophy); Linear programming; Artificial intelligence; Genetic algorithm; Machine learning; Data mining; Algorithm","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":[0.001011845,0.0007560152,0.0009095537,0.0009647733,0.0004976262,0.001246024,0.002286764,0.001794127,0.003443153],"category_scores_gemma":[0.003129959,0.000695413,0.001069289,0.001424272,0.001172289,0.0009034477,0.0009241975,0.001691838,0.0005007906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001205786,"about_ca_system_score_gemma":0.001182338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01149275,"about_ca_topic_score_gemma":0.008448435,"domain_scores_codex":[0.999363,0.0002735716,0.00002284742,0.0001061895,0.0001780402,0.00005643304],"domain_scores_gemma":[0.9987152,0.0009917181,0.00005072123,0.00005012959,0.0001644699,0.00002790951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001567777,0.00007334191,0.0002730258,0.00005020494,0.00005685824,0.00006692841,0.00006943158,0.8544517,0.0008527457,0.05881852,0.001680708,0.08359089],"study_design_scores_gemma":[0.00000690807,0.00001265814,0.00004205576,0.000007902246,0.0000120157,0.00001490575,0.000008230306,0.9692943,0.0002209937,0.0295449,0.0008292979,0.000005832677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003195029,0.0001809081,0.9922823,0.0002066796,0.00004140149,0.00002652866,0.00002126639,0.0002112309,0.003834541],"genre_scores_gemma":[0.1479842,0.0005754015,0.8362041,0.0004398296,0.0001414755,0.0002382705,0.0001240749,0.0001565049,0.01413618],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01149275,"threshold_uncertainty_score":0.02285171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01486095216302931,"score_gpt":0.226617266129212,"score_spread":0.2117563139661827,"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."}}