{"id":"W2798248638","doi":"10.1145/3190619.3190637","title":"Robustness of deep autoencoder in intrusion detection under adversarial contamination","year":2018,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Autoencoder; Robustness (evolution); Computer science; Artificial intelligence; Adversarial system; Deep learning; Machine learning; Intrusion detection system; Adversarial machine learning; Anomaly detection; Artificial neural network; Data mining; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003416909,0.001025523,0.0008355125,0.000537731,0.0003380493,0.0008371873,0.000945892,0.001108929,0.0005258755],"category_scores_gemma":[0.01373324,0.0004235768,0.0005553052,0.0002943144,0.00140338,0.001433695,0.001279884,0.001796407,0.0001702285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009155213,"about_ca_system_score_gemma":0.001007592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003951458,"about_ca_topic_score_gemma":0.001902452,"domain_scores_codex":[0.9984268,0.0004188826,0.0001120966,0.00038057,0.0004280448,0.0002336042],"domain_scores_gemma":[0.9937913,0.004135673,0.000440764,0.0006979299,0.0007681125,0.0001662253],"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":[0.0003848512,0.000150756,0.005193494,0.00008930286,0.0001397064,0.0001456149,0.00008893554,0.9232448,0.01253628,0.002614549,0.000388606,0.05502302],"study_design_scores_gemma":[0.000004052117,0.00006765893,0.0007264063,0.000008471748,0.00001127752,0.00003344764,0.000008462687,0.9921595,0.006072513,0.0008050636,0.00009535327,0.000007627952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4679576,0.00081019,0.5253615,0.0005153462,0.0001195825,0.00008307706,0.0001349522,0.002069476,0.00294829],"genre_scores_gemma":[0.9666443,0.0001907838,0.03230046,0.0001066204,0.00001324182,0.00002705115,0.000117172,0.00003359713,0.0005667021],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003951458,"threshold_uncertainty_score":0.01807058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0100241664206916,"score_gpt":0.2301784300217925,"score_spread":0.2201542636011009,"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."}}