{"id":"W4385729728","doi":"10.1109/itc-egypt58155.2023.10206134","title":"A Modified Transformer Neural Network (MTNN) for Robust Intrusion Detection in IoT Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Denial-of-service attack; Spoofing attack; Intrusion detection system; Computer security; Recurrent neural network; Botnet; Artificial neural network; Internet of Things; Transformer; Computer network; Artificial intelligence; Machine learning; The Internet; Engineering","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":[0.0005491433,0.0006604925,0.0005108641,0.0005916125,0.0002783898,0.0005579846,0.001034819,0.0006316578,0.001060108],"category_scores_gemma":[0.001489648,0.0002214609,0.0005637344,0.0005746388,0.0002754799,0.00107353,0.0005514478,0.000699127,0.0003242843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008016752,"about_ca_system_score_gemma":0.0006076171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009063688,"about_ca_topic_score_gemma":0.006934382,"domain_scores_codex":[0.9997248,0.00004213052,0.00002410646,0.00008940006,0.00007252912,0.00004702434],"domain_scores_gemma":[0.9996678,0.0001034626,0.00003832634,0.00002543163,0.0001479052,0.00001712711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004174162,0.0001921642,0.004834711,0.0001505156,0.0001443736,0.0002635606,0.00007364352,0.472497,0.02112998,0.00302539,0.003819893,0.4934513],"study_design_scores_gemma":[0.000003468761,0.00004028251,0.0003359389,0.000004082972,0.00001463324,0.00003917383,0.000005059345,0.9963403,0.002374305,0.0005993559,0.0002385944,0.0000047992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.118126,0.001414343,0.8714651,0.0004358349,0.0002739129,0.0001047994,0.0002604074,0.003594533,0.004325041],"genre_scores_gemma":[0.9306605,0.0005397237,0.06512744,0.0001957353,0.00005138457,0.00006270185,0.0003357345,0.00005462165,0.002972221],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009063688,"threshold_uncertainty_score":0.01802188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02543507328569837,"score_gpt":0.2380193709214386,"score_spread":0.2125842976357403,"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."}}