STATOS:在低资源环境中用于安全恶意软件分析和样本采集的便携式工具

IF 2.3 Q2 COMPUTER SCIENCE, THEORY & METHODS
Array Pub Date : 2025-04-01 DOI:10.1016/j.array.2025.100391
Alexander Cameron , Abu Alam , Nasreen Anjum , Javed Ali Khan , Alexios Mylonas
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引用次数: 0

摘要

恶意软件对全球范围内的组织构成了重大的安全威胁,特别是在资源有限的环境中。静态分析已经成为深入了解恶意软件的关键技术,但它通常需要专门的硬件和软件,这可能成为面临财务或供应限制的组织的障碍。为了应对这些挑战,本研究提出了一种静态分析操作系统(StatOS),一种专为静态恶意软件分析而设计的便携式Linux衍生操作系统。StatOS可以从USB设备执行,允许组织执行高效,用户友好和安全的恶意软件分析,即使在低功耗的硬件。本研究为低资源环境下的恶意软件现场分析提供了实用的解决方案,为便携式网络安全工具的未来发展提供了模型和需求数据。该工具通过专家反馈(使用德尔菲法)和安全评估(包括蒙特卡罗模拟和常见漏洞和暴露(CVE)评估)相结合进行验证。结果表明,StatOS达到并超过了关键性能要求,100%的受访网络专家同意其有效性,80%的人表示他们将在法医调查中使用StatOS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
STATOS: A portable tool for secure malware analysis and sample acquisition in low resource environments
Malware poses a significant security threat to organisations worldwide, particularly in environments with limited resources. Static analysis has emerged as a crucial technique for gaining insights into malware, but it often requires specialised hardware and software, which can be a barrier for organisations facing financial or supply constraints. To address these challenges, this study presents a Static-Analysis Operating System (StatOS), a portable Linux derivative operating system designed for static malware analysis. StatOS can be executed from a USB device, allowing organisations to perform efficient, user-friendly, and secure malware analysis even on underpowered hardware. This study contributes a practical solution to field analysis of malware within low-resource environments, providing a model and requirement data for future developments in portable cybersecurity tools. The tool was validated through a combination of expert feedback using the Delphi method and security assessments, including Monte-Carlo simulations and Common Vulnerabilities and Exposures (CVE) evaluations. Results indicate that StatOS meets and exceeds key performance requirements, with 100% of surveyed cyber specialists agreeing on its effectiveness, and 80% indicating they would use StatOS in forensic investigations.
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来源期刊
Array
Array Computer Science-General Computer Science
CiteScore
4.40
自引率
0.00%
发文量
93
审稿时长
45 days
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