{"id":"W4287095742","doi":"10.18280/ijsse.120314","title":"Analysis of Autoencoder Compression Performance in Intrusion Detection System","year":2022,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut Teknologi Sepuluh Nopember","keywords":"Autoencoder; Computer science; Intrusion detection system; Data mining; Process (computing); Reduction (mathematics); Intrusion; Artificial neural network; Artificial intelligence; Mathematics","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":[0.001062511,0.0005860495,0.000459655,0.0007278052,0.0003390065,0.0004653585,0.0003735297,0.0005490794,0.001583826],"category_scores_gemma":[0.0048801,0.0001662023,0.0003305429,0.0004107256,0.0003411487,0.0008020779,0.0002954888,0.0006001221,0.0002961448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005513401,"about_ca_system_score_gemma":0.0005094371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005007205,"about_ca_topic_score_gemma":0.002675702,"domain_scores_codex":[0.9991091,0.000103718,0.00006752074,0.0001484614,0.0003920577,0.0001790392],"domain_scores_gemma":[0.9972411,0.001593182,0.0001319946,0.0001420621,0.0008245697,0.00006709617],"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":[0.002721562,0.0005103801,0.02295916,0.0004036026,0.0002095035,0.000654475,0.0003633611,0.5662079,0.06852609,0.003024363,0.00331351,0.3311061],"study_design_scores_gemma":[0.00001304193,0.0003836802,0.01377372,0.00001940376,0.00003743427,0.0001791378,0.00005662825,0.9586474,0.02603607,0.000379677,0.0004545714,0.00001924824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8938323,0.001517763,0.09764507,0.0003723469,0.0001786329,0.00005846161,0.000187335,0.0009436954,0.005264467],"genre_scores_gemma":[0.9901372,0.0002729932,0.008255349,0.00003539997,0.00001619446,0.00002684887,0.0001888414,0.00003167129,0.001035417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005007205,"threshold_uncertainty_score":0.009956121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004511144746502157,"score_gpt":0.1981504547183251,"score_spread":0.1936393099718229,"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."}}