{"id":"W4309750135","doi":"10.21203/rs.3.rs-2284207/v1","title":"A Survey of Advancement in AnomalyIntrusion Detection System","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of New South Wales; Universitas Bengkulu","keywords":"Intrusion detection system; Computer science; Feature selection; Machine learning; Hacker; Artificial intelligence; Identification (biology); Anomaly-based intrusion detection system; Anomaly detection; Selection (genetic algorithm); Data mining; The Internet; Intrusion; Computer security; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002428293,0.0006860766,0.0009487397,0.005791896,0.0005739207,0.001824372,0.001588412,0.0007002011,0.001600323],"category_scores_gemma":[0.006012004,0.0004732886,0.0008113327,0.005995085,0.000390856,0.003171695,0.0008544775,0.001009835,0.001100113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009767105,"about_ca_system_score_gemma":0.001045643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0031071,"about_ca_topic_score_gemma":0.001404807,"domain_scores_codex":[0.996351,0.0005185676,0.0004175707,0.0008864648,0.001630084,0.0001962411],"domain_scores_gemma":[0.9957821,0.001324739,0.0002483478,0.0004076014,0.002127251,0.0001099958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001314345,0.0001542851,0.01368939,0.0010529,0.00009997949,0.00009336005,0.0001560372,0.004220198,0.004622866,0.004650192,0.01335242,0.957777],"study_design_scores_gemma":[0.00004190049,0.001321949,0.04455768,0.001428807,0.0004659146,0.002697603,0.001058879,0.2350309,0.07692282,0.01644789,0.619719,0.0003068028],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.1672999,0.3506461,0.4128596,0.005682368,0.002290787,0.0005966178,0.004448619,0.006193549,0.04998254],"genre_scores_gemma":[0.6085472,0.1942337,0.1755475,0.001271554,0.001322888,0.0003189076,0.007175198,0.0003260437,0.01125705],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005791896,"threshold_uncertainty_score":0.01284218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07128568874134285,"score_gpt":0.3664205371898966,"score_spread":0.2951348484485538,"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."}}