{"id":"W7133547427","doi":"10.1109/aisummit66170.2025.11410657","title":"Enhancing Malware Detection with Malware-BERT: A Hybrid Approach Using Multi-Head Attention","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Malware; Feature (linguistics); Key (lock); Intrusion detection system; Set (abstract data type)","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","sts"],"consensus_categories":[],"category_scores_codex":[0.001008331,0.001006261,0.0008510372,0.001360008,0.001333675,0.0007788072,0.001183185,0.0003899893,0.00005555618],"category_scores_gemma":[0.0001684239,0.0009948963,0.0003451364,0.003199641,0.0002352029,0.002810519,0.0009181315,0.001058096,0.00003884598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001204382,"about_ca_system_score_gemma":0.0004607103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007040683,"about_ca_topic_score_gemma":0.0006594023,"domain_scores_codex":[0.9936038,0.0004628885,0.001300246,0.0025008,0.0008320899,0.001300213],"domain_scores_gemma":[0.9961428,0.0001510068,0.0006745541,0.001834357,0.0009196118,0.0002776726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006646268,0.001593524,0.002591406,0.001868082,0.000522885,0.0001540779,0.0007146706,0.007749836,0.3692621,0.002658288,0.00005408233,0.6121665],"study_design_scores_gemma":[0.00116608,0.0003610498,0.00148441,0.0007442459,0.0001256269,0.0003932213,0.0002771461,0.5480658,0.4457474,0.0003475652,0.000399235,0.0008882578],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03479457,0.0003884147,0.9578211,0.0001008886,0.001259493,0.001945739,0.000009248265,0.002180023,0.001500538],"genre_scores_gemma":[0.5762441,0.00003366978,0.4202459,0.00021661,0.00008408815,0.000141643,0.00000508171,0.00005731458,0.00297158],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6112782,"threshold_uncertainty_score":0.9999664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0226546332454012,"score_gpt":0.2864941457564656,"score_spread":0.2638395125110644,"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."}}