基于手机沙盒的Android安全性分析

Munishka Vijayvergiya, Abhignya Tayi, Sanyam Jain, Sanjana Reddy
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引用次数: 0

摘要

如今,智能手机和Android设备正以前所未有的速度飞速发展,成为最容易发生网络犯罪的平台。如果我们仔细观察对安全和防御的危险,安全专家有必要调查为这些框架编写的报复性编程。本文的主要目的是描述Mobile Sandbox,据说这是一个旨在以新方式定期检查Android应用程序的平台。首先,在用于处理动态调查的静态分析的后续效果的本质上,它结合了静态和动态检查,并试图证明引入已执行代码的合理性。另一方面,为了记录对本机api的调用,它使用这些技术,最后,它将最终结果与机器学习技术结合起来,收集分析成危险的样本。我们审查了来自亚洲国际商业领域的69000多份申请,发现其中约21%的申请在其代码中正式使用本地呼叫。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Android Security Analysis using Mobile Sandbox
Today, smartphones and Android devices are effectively in development like never before and have become the easiest cybercrime forum. It is necessary for security experts to investigate the vengeful programming composed for these frameworks if we closely observe the danger to security and defence. The main objective of this paper was to describe Mobile Sandbox, which is said to be a platform intended to periodically examine Android applications in new ways. First of all in the essence of the after-effects of static analysis that is used to handle the dynamic investigation, it incorporates static and dynamic examination and attempts to justify the introduction of executed code. On the other hand, to log calls to native APIs, it uses those techniques, and in the end, it combines the end results with machine learning techniques to collect the samples analysed into dangerous ones. We reviewed the platform for more than 69, 000 applications from multi-talented Asian international businesses sectors and found that about 21% of them officially use the local calls in their code.
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