{"id":"W2991435551","doi":"10.1002/ett.3803","title":"DL‐IDS: a deep learning–based intrusion detection framework for securing IoT","year":2019,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":335,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Intrusion detection system; Denial-of-service attack; Computer network; Computer security; Artificial intelligence; Internet of Things; Deep learning; Wireless network; Wearable computer; Wireless; Machine learning; The Internet; Embedded system; Telecommunications","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":[0.0007859235,0.0007887035,0.000739028,0.000689649,0.0002599417,0.0006062323,0.00149684,0.0007841064,0.001144697],"category_scores_gemma":[0.001354651,0.0003473205,0.0007089944,0.0004203279,0.0005124486,0.0009732943,0.001162532,0.001358701,0.0003395314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008320537,"about_ca_system_score_gemma":0.001057483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004945707,"about_ca_topic_score_gemma":0.00498378,"domain_scores_codex":[0.999635,0.0000696565,0.00002837884,0.00009872511,0.00009906341,0.00006918485],"domain_scores_gemma":[0.9996427,0.0001159419,0.0000514998,0.00003480679,0.0001228945,0.0000322025],"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":[0.0002788062,0.0003558878,0.006192641,0.0001692095,0.000209346,0.000194328,0.00008991904,0.6295966,0.009750061,0.008896597,0.009835729,0.3344309],"study_design_scores_gemma":[0.000003355621,0.00001677038,0.0001168443,0.000003161075,0.000005569042,0.000009059581,0.000002746737,0.9973514,0.0008359074,0.001293682,0.0003587636,0.000002834915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02758452,0.0007183562,0.9650155,0.0005155114,0.00009197752,0.00006750759,0.000242313,0.004437418,0.001326832],"genre_scores_gemma":[0.7065277,0.0005695376,0.2865138,0.0007691453,0.00009057695,0.000194145,0.001077078,0.0001271276,0.004130809],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004945707,"threshold_uncertainty_score":0.009833813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01281290440659908,"score_gpt":0.2578505190139471,"score_spread":0.245037614607348,"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."}}