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Deep Learning for Malware Detection: TransformerBased Analysis of Windows Executables

Year: 2026

Type: conference-paper

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.

Source: Republican Scientific-Practical Conference "Actual Problems of Cybersecurity and Digital Forensics"

DOI: 10.25045/forensic.gov.2026.18

Author: Elshan Baghirov

Institution: Institute of Information Technology

Language: English

Topic: Advanced Malware Detection Techniques

Subfield: Software

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W7203840205

FWCI:

Cites: 11

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Related to: 10