{
  "id": 19057,
  "url": "https://arxiv.org/abs/2608.12077v1",
  "title": "A Comparison of Malware Image Transformations Using Grad-CAM and Hybrid Learning Models",
  "summary": "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,",
  "authors": "Vibha Bhavikatti, Mark Stamp",
  "category": "research",
  "topics": "safety-alignment,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T14:00:44.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "x-arxiv-cs-cr-ai-security",
  "source_name": "arXiv cs.CR (AI security)",
  "source_homepage": "https://arxiv.org/list/cs.CR/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/19057",
  "original_url": "https://arxiv.org/abs/2608.12077v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}