Aug 2026· “Kibertəhlükəsizlik və rəqəmsal kriminalistikanın aktual problemləri” respublika elmi-praktiki konfransı· 0 citations
TL;DR
A Transformerbased deep learning approach for detecting malicious Windows Portable Executable files is presented, significantly outperforming baseline methods including LightGBM, MalConv, and LSTM.
Abstract
Malware detection is challenged by unprecedented threats exceeding 560,000 new variants daily. A Transformerbased deep learning approach for detecting malicious Windows Portable Executable files is presented. Raw byte sequences are processed using self-attention mechanisms, eliminating manual
feature engineering. A multi-modal framework combining raw bytes, PE metadata, and entropy features is implemented. Through experimental evaluation on EMBER2024 (3.2M samples)
and SOREL-20M (20M samples), 95.1% accuracy with 0.4% false positive rate is demonstrated, significantly outperforming baseline methods including LightGBM (92.7%), MalConv (93.4%), and
LSTM approaches (91.8%). Explainability for forensic analysis is provided through attention visualization.
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