{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009209818,0.0002458434,0.0002583369,0.0005142116,0.001191895,0.0001811137,0.0007708187,0.0001839211,0.00008162],"category_scores_gemma":[0.00004310025,0.0002809278,0.0001728014,0.0009440713,0.00003270475,0.001613584,0.00003260172,0.001579989,0.00001550898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006823122,"about_ca_system_score_gemma":0.0002968763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008636279,"about_ca_topic_score_gemma":0.000005806324,"domain_scores_codex":[0.9976349,0.0003359583,0.0007313458,0.0002377967,0.0007144905,0.0003455488],"domain_scores_gemma":[0.9986905,0.000226931,0.0003971192,0.0004917301,0.00008811231,0.0001055901],"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":[0.00013396,0.00004456062,8.555514e-7,0.00000619475,0.00003608498,0.000001491512,0.003070024,0.9044388,0.0001715897,0.0002810151,0.00003033315,0.0917851],"study_design_scores_gemma":[0.001257495,0.0002031217,3.411172e-7,0.00001420823,0.00005318201,0.00004467879,0.0008994177,0.9930248,0.003555829,0.0003717378,0.0002942542,0.0002808993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00481342,0.000001436083,0.9898179,0.00009260602,0.004143758,0.0003976637,0.00002946645,0.000320737,0.0003830388],"genre_scores_gemma":[0.7981812,8.962716e-7,0.2012936,0.0002253601,0.0001231715,0.0000564406,0.00000439933,0.00002157511,0.0000933183],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7933678,"threshold_uncertainty_score":0.9999643,"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."}}