Bytes to Threats: Byte-Sequence Transformer For Malware Detection
This paper presents MalwareFormer, a transformer-based, byte-level malware detection framework that operates directly on raw bytes, without requiring domain knowledge or feature extraction.
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Key points
- Existing malware detection approaches heavily rely on handcrafted features derived from static or dynamic analysis of the executable files.
- Consequently, there is a growing interest in approaches that operate directly on raw bytes, which contain rich semantic and structural information.
- However, capturing long-range dependencies within executable files remains challenging for conventional static and CNN-based methods.
- MalwareFormer achieves an accuracy of 98.35%, a balanced accuracy of 98.32%, a macro-F1 of 98.32%, and a false positive rate of 1.94%, outperforming both baselines across all threshold-dependent classification metrics and demonstrating that transformers provide a powerful and scalable approach for byte-level malware detection.
Sources (1)
- [1]Bytes to Threats: Byte-Sequence Transformer For Malware DetectionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 05:37 PM
This paper presents MalwareFormer, a transformer-based, byte-level malware detection framework that operates directly on raw bytes, without requiring domain knowledge or feature extraction.
Existing malware detection approaches heavily rely on handcrafted features derived from static or dynamic analysis of the executable files.
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