{"id":"W4387969881","doi":"10.1109/cns59707.2023.10288658","title":"The Impact of Dynamic Learning on Adversarial Attacks in Networks (IEEE CNS 23 Poster)","year":2023,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Adversarial system; Computer science; Adversarial machine learning; Vulnerability (computing); Focus (optics); Intrusion detection system; Field (mathematics); Intrusion; Computer security; Machine learning; Artificial intelligence; Vulnerability assessment","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":[0.003989799,0.0007179131,0.0006337183,0.0007624635,0.000997339,0.00203591,0.0007768642,0.001413576,0.004713727],"category_scores_gemma":[0.0160531,0.0002923295,0.0004110427,0.0005070202,0.001949563,0.002849102,0.002060724,0.002596408,0.000719099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001702104,"about_ca_system_score_gemma":0.0007058713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001894764,"about_ca_topic_score_gemma":0.001975875,"domain_scores_codex":[0.9981489,0.0006976004,0.00005587739,0.0002373735,0.0005732443,0.0002869723],"domain_scores_gemma":[0.9842597,0.01196598,0.0006348878,0.001228893,0.001326233,0.0005843962],"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":[0.0004335294,0.0001382727,0.003000134,0.0001465721,0.0001139995,0.0002978567,0.0001737593,0.7254096,0.005590428,0.08215049,0.01915758,0.1633878],"study_design_scores_gemma":[0.00001513381,0.0002648947,0.001643248,0.00006155878,0.00003207215,0.0002711328,0.0001021553,0.919076,0.005135179,0.06752246,0.005843687,0.0000324173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2956979,0.01075131,0.5570384,0.02755523,0.00306722,0.0002091895,0.0005289477,0.001702803,0.1034491],"genre_scores_gemma":[0.966702,0.002853357,0.01990638,0.0007614344,0.0004972158,0.00004837879,0.0001390408,0.0001253135,0.008966844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004713727,"threshold_uncertainty_score":0.02110034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01212484579488327,"score_gpt":0.2775020674245278,"score_spread":0.2653772216296445,"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."}}