{"id":"W2943632713","doi":"10.25046/aj040334","title":"A Support Vector Machine Cost Function in Simulated Annealing for Network Intrusion Detection","year":2019,"lang":"en","type":"article","venue":"Advances in Science Technology and Engineering Systems Journal","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Simulated annealing; Support vector machine; Intrusion detection system; Computer science; Data mining; Artificial intelligence; 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":[0.0007106961,0.0004288557,0.0006943144,0.0002967157,0.0002539718,0.000459893,0.0006382159,0.0007732316,0.001586308],"category_scores_gemma":[0.002803048,0.0002033482,0.0004577228,0.0003821281,0.0002811417,0.000593263,0.0003488491,0.001028395,0.0002280757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000643447,"about_ca_system_score_gemma":0.00067169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00449613,"about_ca_topic_score_gemma":0.003317541,"domain_scores_codex":[0.9997224,0.0001370671,0.00001232128,0.00003899735,0.0000643797,0.00002482362],"domain_scores_gemma":[0.9988708,0.0008056385,0.00005949326,0.00005418174,0.0001848579,0.00002496464],"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":[0.00004321395,0.0000411168,0.0006658636,0.00003299609,0.00002296365,0.00001288114,0.00001426479,0.9705345,0.0007687336,0.002196323,0.0003468734,0.02532028],"study_design_scores_gemma":[0.000001079073,0.00001267995,0.000064739,0.00000123334,0.000001948737,0.000002564927,0.000001254466,0.9994343,0.0001600314,0.0002539091,0.00006533025,8.673045e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1141976,0.0008691739,0.8797388,0.0003715011,0.0001227288,0.00006899566,0.00006622136,0.0006057584,0.003959296],"genre_scores_gemma":[0.8663687,0.0001878795,0.1303769,0.00005469352,0.00002971073,0.0001293192,0.00009792005,0.00006848828,0.002686437],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00449613,"threshold_uncertainty_score":0.008939922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004713577558689217,"score_gpt":0.22690292557288,"score_spread":0.2221893480141907,"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."}}