{"id":"W3164510030","doi":"10.1109/access.2021.3083421","title":"Security Hardening of Botnet Detectors Using Generative Adversarial Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"Engineering and Physical Sciences Research Council; Northumbria University","keywords":"Botnet; Computer science; Adversarial system; Artificial intelligence; Machine learning; Malware; Emulation; Test set; Classifier (UML); Oversampling; Data mining; Computer security; Computer network; The Internet; Bandwidth (computing); World Wide Web","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":[0.0003439065,0.0001969807,0.0003270875,0.00009484532,0.00022578,0.0002792089,0.001262137,0.0001322383,0.00005135238],"category_scores_gemma":[0.0002073518,0.000209821,0.0001170434,0.0007737438,0.00007300352,0.001183672,0.0007937635,0.0004094816,0.000002271804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008146762,"about_ca_system_score_gemma":0.0002151787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001893289,"about_ca_topic_score_gemma":0.00005176021,"domain_scores_codex":[0.9980695,0.0003210392,0.0003581913,0.0005190076,0.0003719922,0.0003603192],"domain_scores_gemma":[0.9985986,0.000209053,0.0002873258,0.0005689965,0.0002436917,0.00009234377],"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":[0.0000146653,0.00002786965,0.003886415,0.0000159408,0.00005526613,0.0000920045,0.0006815069,0.988529,0.003081597,0.001281605,0.0001280819,0.002206092],"study_design_scores_gemma":[0.0004702439,0.00001980552,0.0005631884,0.00004321602,0.000024341,0.00002300493,0.00003720159,0.9595939,0.03785007,0.0009787614,0.0001517127,0.0002445518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2081645,0.0001299548,0.7886629,0.00005511981,0.002469999,0.00008037227,0.00000175141,0.00008175454,0.0003536762],"genre_scores_gemma":[0.9574799,0.000008457824,0.04165176,0.0001268969,0.0006854702,0.000003382592,0.000003072369,0.0000181293,0.00002291008],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7493154,"threshold_uncertainty_score":0.8556252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0323627462343424,"score_gpt":0.3187290371346199,"score_spread":0.2863662909002775,"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."}}