{"id":"W4390570531","doi":"10.1007/978-981-99-9614-8_10","title":"Adversarial Example Attacks and Defenses in DNS Data Exfiltration","year":2024,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Adversarial system; Computer security; Network packet; Protocol (science); Phone; The Internet; Voting; Internet privacy; Computer network; World Wide Web; Artificial intelligence","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":[0.0009546503,0.0005837693,0.0004544855,0.0006542899,0.000693572,0.002149852,0.0008332597,0.001610398,0.00451886],"category_scores_gemma":[0.004195073,0.0003486257,0.0003918622,0.0007207793,0.001933719,0.00295102,0.001308725,0.002445324,0.0007226149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008979081,"about_ca_system_score_gemma":0.0002505527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005762766,"about_ca_topic_score_gemma":0.0005640449,"domain_scores_codex":[0.9991294,0.0003460647,0.00002622108,0.00008630438,0.000315434,0.00009665734],"domain_scores_gemma":[0.9980604,0.001353969,0.0001037084,0.0002981084,0.0001386939,0.00004506562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002231392,0.00008414501,0.0005021619,0.0001237653,0.00003259181,0.0002286827,0.0001974432,0.1007777,0.003152904,0.7865231,0.01464386,0.09351063],"study_design_scores_gemma":[0.00001791095,0.00009206494,0.0004822223,0.00008930665,0.00002548647,0.0006425125,0.0001181348,0.509188,0.004347426,0.4573537,0.0276149,0.00002845988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06336016,0.009512157,0.6753002,0.005008228,0.000814618,0.0001256017,0.0001491702,0.0009745675,0.2447553],"genre_scores_gemma":[0.8417389,0.006071267,0.08050029,0.0006386513,0.0004964087,0.00007485472,0.0001294448,0.0001390511,0.07021112],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00451886,"threshold_uncertainty_score":0.01511711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07848318458146727,"score_gpt":0.3043162390752341,"score_spread":0.2258330544937669,"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."}}