{"id":"W3094495145","doi":"10.18280/ria.340410","title":"A Deep Learning Approach to Network Intrusion Detection Using Deep Autoencoder","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoencoder; Computer science; Deep learning; Intrusion detection system; Artificial intelligence; Machine learning; Network security; Graphics; Artificial neural network; Data mining; Computer security","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004873625,0.0002501319,0.000276675,0.0001253956,0.0007626241,0.0002763964,0.0007708335,0.000156457,0.00008997454],"category_scores_gemma":[0.0001716953,0.0002659568,0.0001346899,0.001912171,0.00004791081,0.0005226652,0.0005105866,0.0005509064,0.0005980498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009757225,"about_ca_system_score_gemma":0.00002595107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003635244,"about_ca_topic_score_gemma":0.00001202035,"domain_scores_codex":[0.9975363,0.0002016985,0.0005274201,0.0008470956,0.0003038651,0.0005836246],"domain_scores_gemma":[0.9988075,0.00008893426,0.0001680802,0.0004601842,0.0001399665,0.000335322],"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":[0.00002210574,0.00003625248,0.00001796278,0.00002164809,0.000007260639,0.000003867618,0.003229785,0.8288366,0.002758723,0.002356012,0.00003352924,0.1626763],"study_design_scores_gemma":[0.00003351846,0.0002442932,0.00001028053,0.00003584822,0.000009382055,0.00004220083,0.0002736552,0.9784122,0.008716458,0.001194407,0.0107145,0.0003132213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009511936,0.0002725375,0.9864373,0.0004805934,0.0006060154,0.000367478,1.7504e-7,0.0004302085,0.001893726],"genre_scores_gemma":[0.8839754,0.00006360135,0.1141237,0.0008962789,0.0008111792,0.00002698194,0.00000256251,0.00002847643,0.00007183464],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8744634,"threshold_uncertainty_score":0.9999793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04144278820418707,"score_gpt":0.2503232743724293,"score_spread":0.2088804861682423,"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."}}