{"id":"W7125630418","doi":"10.1109/cascon66301.2025.00047","title":"Data Augmentation with RNN-Driven CGANs for RF Jamming Intrusion Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Security in Wireless Sensor Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brock University","funders":"","keywords":"Intrusion detection system; Noise (video); Pattern recognition (psychology); Jamming; Intrusion","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":[0.0006353959,0.0007243237,0.0004613299,0.0003346826,0.0003146287,0.000377468,0.0008166902,0.0004930987,0.00155594],"category_scores_gemma":[0.002269858,0.0002107789,0.0002869416,0.0003885164,0.0003495937,0.0007624145,0.0009373539,0.001153903,0.0004956052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003523417,"about_ca_system_score_gemma":0.0006561808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003995133,"about_ca_topic_score_gemma":0.007434795,"domain_scores_codex":[0.9996626,0.00006997335,0.00001550269,0.00007870842,0.0001227525,0.00005043723],"domain_scores_gemma":[0.9992285,0.0002634449,0.00004698216,0.0001248559,0.0003070264,0.00002919947],"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":[0.001010358,0.0003058199,0.002294386,0.0001306222,0.00007608401,0.0001682169,0.0001433415,0.2789681,0.08205016,0.005009498,0.007062458,0.622781],"study_design_scores_gemma":[0.000008722717,0.00004979987,0.0004170116,0.000007614055,0.00001047133,0.00003140006,0.00001070592,0.9849626,0.01222491,0.001003335,0.001265305,0.000008160615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06154293,0.000507058,0.9306794,0.0003756884,0.0004988553,0.00008463399,0.0002212984,0.002774524,0.003315642],"genre_scores_gemma":[0.7635291,0.0001945525,0.2315981,0.0003546068,0.0001326103,0.00009127036,0.0004372581,0.000174671,0.003487795],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003995133,"threshold_uncertainty_score":0.007943749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03131631250196598,"score_gpt":0.2948900003694223,"score_spread":0.2635736878674563,"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."}}