{"id":"W4286377411","doi":"10.1109/tii.2022.3192901","title":"Adversarial ELF Malware Detection Method Using Model Interpretation","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Adversarial system; Malware; Computer science; Adversarial machine learning; Executable; Artificial intelligence; Byte; Machine learning; Key (lock); Interpretation (philosophy); Anomaly detection; Data mining; Computer security","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":[0.0008810318,0.00115601,0.0008908021,0.001180606,0.0003410458,0.0007107865,0.001111154,0.0009340015,0.001514048],"category_scores_gemma":[0.002686277,0.0003450385,0.00102394,0.0003472862,0.0008806617,0.001274972,0.001208076,0.001864847,0.0004688873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007362893,"about_ca_system_score_gemma":0.000678646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001369481,"about_ca_topic_score_gemma":0.001156239,"domain_scores_codex":[0.9992667,0.0001504168,0.00003212347,0.0001730478,0.000280263,0.00009743932],"domain_scores_gemma":[0.9988324,0.0005067831,0.0001700802,0.0002192371,0.0002286171,0.0000428977],"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":[0.0001699623,0.0001217467,0.002532396,0.00008134857,0.00009228125,0.0004264151,0.0001483251,0.7219633,0.01741213,0.01664793,0.003102763,0.2373013],"study_design_scores_gemma":[0.000001936802,0.0000141288,0.00008348558,0.000002443684,0.000004094248,0.00005104263,0.000003952509,0.9943457,0.002294488,0.00294312,0.000250892,0.000004711184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02227579,0.0001302121,0.9742798,0.0001831538,0.00003234483,0.00004697933,0.00003751679,0.001784444,0.001229832],"genre_scores_gemma":[0.7769796,0.0002122956,0.2184407,0.0003124268,0.00006823974,0.000102986,0.000265775,0.0002171943,0.003400788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001514048,"threshold_uncertainty_score":0.005342245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05143939852851373,"score_gpt":0.3004563836992305,"score_spread":0.2490169851707168,"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."}}