{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006099653,0.0005910717,0.0005036708,0.0004371027,0.0002745688,0.0006254751,0.000892984,0.0008011502,0.001386149],"category_scores_gemma":[0.001159219,0.0003969046,0.0006029814,0.00038996,0.0004426288,0.0008176569,0.000729325,0.001868199,0.0003895229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007582505,"about_ca_system_score_gemma":0.0008947614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007937791,"about_ca_topic_score_gemma":0.009731655,"domain_scores_codex":[0.9997929,0.00004486218,0.00001395108,0.00004660868,0.00006506575,0.00003661034],"domain_scores_gemma":[0.9996561,0.00012703,0.0000259057,0.0000401642,0.0001282358,0.00002259398],"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":[0.00006803274,0.00008013975,0.000904757,0.00005148434,0.0000819649,0.00007086615,0.00005777658,0.8242144,0.007984773,0.006898018,0.001766543,0.1578212],"study_design_scores_gemma":[0.00000113398,0.00001098176,0.0000673683,0.000002312419,0.000002816588,0.000005950158,0.0000018729,0.9978987,0.0008338959,0.0009381624,0.0002348626,0.000001864938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02314394,0.0004638508,0.9730301,0.0003520352,0.00007565357,0.00003381537,0.00007203605,0.001184475,0.001644085],"genre_scores_gemma":[0.6176341,0.0007250843,0.3716964,0.0002875366,0.0001042125,0.0001147428,0.0003837247,0.0001440082,0.008910136],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007937791,"threshold_uncertainty_score":0.01578319,"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."}}