Feature Engineering Using File Layout for Malware Detection

Jeongwoo Kim; Eun-Sun Cho; Joon-Young Paik

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arXiv · 2304.02260

Feature Engineering Using File Layout for Malware Detection

Jeongwoo Kim·Eun-Sun Cho·Joon-Young Paik

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Abstract

Malware detection on binary executables provides a high availability to even binaries which are not disassembled or decompiled. However, a binary-level approach could cause ambiguity problems. In this paper, we propose a new feature engineering technique that use minimal knowledge about the internal layout on a binary. The proposed feature avoids the ambiguity problems by integrating the information about the layout with structural entropy. The experimental results show that our feature improves accuracy and F1-score by 3.3% and 0.07, respectively, on a CNN based malware detector with realistic benign and malicious samples.

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Jeongwoo Kim, Eun-Sun Cho, Joon-Young Paik. 2023-04-05. Feature Engineering Using File Layout for Malware Detection. https://arxiv.org/abs/2304.02260

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