{"id":"W4402883814","doi":"10.1016/j.jiixd.2024.09.001","title":"An efficient self attention-based 1D-CNN-LSTM network for IoT attack detection and identification using network traffic","year":2024,"lang":"en","type":"article","venue":"Journal of Information and Intelligence","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; York University; University of New Brunswick","funders":"","keywords":"Computer science; Identification (biology); Internet of Things; Artificial intelligence; Computer network; Machine learning; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001441821,0.0001246145,0.0001503443,0.0002385883,0.0003578353,0.0008243154,0.0002013892,0.00009525083,0.000006819968],"category_scores_gemma":[0.00003991499,0.00011104,0.00008265935,0.0005801806,0.00003758856,0.001706281,0.00002589286,0.0002019043,0.000006122406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007809714,"about_ca_system_score_gemma":0.00009535279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003032186,"about_ca_topic_score_gemma":0.000005647589,"domain_scores_codex":[0.9985335,0.00006880157,0.000797954,0.0001441364,0.000257441,0.0001981541],"domain_scores_gemma":[0.9988575,0.0001326691,0.0004190293,0.0001415997,0.0003359612,0.0001132264],"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":[0.00003646074,0.00002076387,0.00001437192,0.00008383906,0.00001630954,6.182479e-7,0.0006236545,0.7470398,0.00018672,0.002447302,0.0001854344,0.2493447],"study_design_scores_gemma":[0.0001032017,0.0003195191,0.0003590138,0.0001818956,0.00002535407,0.00009404226,0.00008502159,0.9914332,0.0007848787,0.0006641019,0.005826101,0.0001236583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2227833,0.0006201157,0.774757,0.0001271227,0.00145639,0.0001805041,0.000001070234,0.0000631832,0.00001123479],"genre_scores_gemma":[0.9656662,0.0002321164,0.03345526,0.000177816,0.0004506103,0.000005240207,0.000002689339,0.000005838444,0.000004186255],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7428829,"threshold_uncertainty_score":0.7948892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0182471457001265,"score_gpt":0.2774175977760905,"score_spread":0.259170452075964,"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."}}