A Comparison of Malware Image Transformations Using Grad-CAM and Hybrid Learning Models
Recent studies have shown that binary-to-image representations can enable effective machine learning-based results for malware detection and classification. However, performance can vary significantly, depending on the technique used to convert binaries to images. Furthermore, the explainability and interpretability of image-based models is largely unexplored within the malware domain. In this research, we employ Gradient-weighted Class Activation Maps (Grad-CAM) as an eXplainable AI (XAI) tool,
Record details
Published: 12 August 2026
Source: arXiv cs.CR (AI security)
Category: Research
Topics: Safety & alignment · Transparency
Retrieved: 13 August 2026
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ethics.ai (12 August 2026), “A Comparison of Malware Image Transformations Using Grad-CAM and Hybrid Learning Models,” evidence record 19057, https://ethics.ai/record/19057 (originally published by arXiv cs.CR (AI security)).
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