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Burnyard: Future of Malware Analysis

arXiv Security Archived Jun 24, 2026 ✓ Full text saved

arXiv:2606.24778v1 Announce Type: new Abstract: Malware analysis is a critical aspect of modern cybersecurity. The prevailing industry practice, sandboxing, involves executing suspicious binaries within isolated virtual machines in large-scale data centers. However, this approach can unintentionally expose samples to public platforms such as VirusTotal and MalwareBazaar, and it is both resource-intensive and time-consuming. Burnyard addresses these limitations through a lightweight binary emulat

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    Computer Science > Cryptography and Security [Submitted on 23 Jun 2026] Burnyard: Future of Malware Analysis Rama Ramana Sharma Parnandi, Carter Yagemann Malware analysis is a critical aspect of modern cybersecurity. The prevailing industry practice, sandboxing, involves executing suspicious binaries within isolated virtual machines in large-scale data centers. However, this approach can unintentionally expose samples to public platforms such as VirusTotal and MalwareBazaar, and it is both resource-intensive and time-consuming. Burnyard addresses these limitations through a lightweight binary emulation platform that captures observable runtime behavior and records it as structured CSV event traces. Comments: 5 pages, 2 figures Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.24778 [cs.CR]   (or arXiv:2606.24778v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.24778 Focus to learn more Submission history From: Carter Yagemann [view email] [v1] Tue, 23 Jun 2026 16:36:40 UTC (678 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Jun 24, 2026
    Archived
    Jun 24, 2026
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