{"id":"W4210935151","doi":"10.1007/978-3-030-91390-8_8","title":"Embedding Time-Series Features into Generative Adversarial Networks for Intrusion Detection in Internet of Things Networks","year":2022,"lang":"en","type":"book-chapter","venue":"Intelligent systems reference library","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Embedding; Computer science; Series (stratigraphy); Intrusion detection system; Adversarial system; Internet of Things; Generative grammar; Artificial intelligence; Theoretical computer science; Machine learning; Computer security; Geology","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":[0.0005693305,0.0006851935,0.0005385948,0.0003670279,0.0001409413,0.0005066526,0.0007695254,0.0006675788,0.001562006],"category_scores_gemma":[0.002106965,0.0003481833,0.0006708826,0.000530984,0.0004247716,0.0009451524,0.0009091414,0.001582087,0.0004917547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004236864,"about_ca_system_score_gemma":0.0002031947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001112291,"about_ca_topic_score_gemma":0.001548809,"domain_scores_codex":[0.9997843,0.00006301447,0.00001077568,0.00005180355,0.00007029698,0.00001983126],"domain_scores_gemma":[0.999095,0.0006861608,0.0000546077,0.00007255292,0.00007812939,0.00001365478],"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":[0.00003952621,0.00003369709,0.0003897278,0.00005149868,0.00005324461,0.00004942317,0.00003839563,0.8681843,0.00308151,0.01215102,0.001533561,0.1143942],"study_design_scores_gemma":[4.859571e-7,0.00000731157,0.00005267253,0.000002245235,0.000003340047,0.00001061118,0.000001271045,0.9961415,0.0004274472,0.003168481,0.0001826957,0.000001869385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01363484,0.0007695125,0.9833472,0.0001885283,0.00009325427,0.00001402154,0.00006244165,0.0005721338,0.001317945],"genre_scores_gemma":[0.7644867,0.002154462,0.2193833,0.0002227652,0.0002278425,0.00008670933,0.0005847271,0.0003054641,0.01254812],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001562006,"threshold_uncertainty_score":0.00522542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01548838225510648,"score_gpt":0.2368813227960577,"score_spread":0.2213929405409512,"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."}}