{"id":"W4388459565","doi":"10.18280/isi.280506","title":"Enhancing Anomaly-Based Intrusion Detection Systems: A Hybrid Approach Integrating Feature Selection and Bayesian Hyperparameter Optimization","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hyperparameter; Feature selection; Anomaly detection; Computer science; Artificial intelligence; Machine learning; Bayesian probability; Intrusion detection system; Selection (genetic algorithm); Data mining; Bayesian optimization; Feature (linguistics); Bayesian inference; Pattern recognition (psychology); Hyperparameter optimization; Support vector machine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003048477,0.001368849,0.001427349,0.00144074,0.0003300385,0.001249924,0.001359513,0.001195056,0.0005680383],"category_scores_gemma":[0.004498991,0.0006537305,0.000921435,0.001097649,0.0006194858,0.001525385,0.001248724,0.00125819,0.0003300442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006994894,"about_ca_system_score_gemma":0.001249376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003497612,"about_ca_topic_score_gemma":0.002675207,"domain_scores_codex":[0.9988863,0.0003999639,0.00006988768,0.0001808178,0.000366194,0.00009689876],"domain_scores_gemma":[0.9984728,0.0007537531,0.0001531591,0.0001200595,0.0004557496,0.00004449131],"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":[0.0001141597,0.0001631175,0.002176247,0.00008371541,0.0001206949,0.00007136976,0.00008704983,0.8222802,0.01003137,0.0030459,0.0006948361,0.1611314],"study_design_scores_gemma":[0.000004855247,0.00004550432,0.0002301732,0.000006930329,0.00001238586,0.00001924862,0.000006202462,0.9967135,0.001472564,0.001127132,0.0003532655,0.000008221551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02473403,0.000340828,0.9732966,0.0001720261,0.00002031723,0.00005899604,0.00002573068,0.0007652121,0.0005862043],"genre_scores_gemma":[0.6520209,0.0004167013,0.345323,0.0002622655,0.00006519275,0.0002373046,0.0001687557,0.0001348494,0.001371043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003497612,"threshold_uncertainty_score":0.0161221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00817729772244981,"score_gpt":0.2023049799808965,"score_spread":0.1941276822584466,"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."}}