使用静态分析检测android中的软件漏洞

R. Dhaya, M. Poongodi
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引用次数: 29

摘要

现在,像智能手机、平板电脑和个人数字助理等移动设备在我们的日常生活中扮演着最重要的角色。高端移动设备的功能与计算机相同。基于Android的智能手机变得更加脆弱,因为它是一个开源的操作系统。任何人都可以开发新的应用程序并将其发布到android市场。这些类型的申请未经授权公司核实。因此,它可能包括恶意应用程序,它可能是病毒,间谍软件,蠕虫等,这可能会导致系统故障,浪费内存资源,破坏数据,窃取个人信息,也增加了维护成本。由于这些原因,手机安全或移动安全是移动计算中非常重要的一个问题。在现有的系统中,由于更新签名的限制,无法检测到新的病毒。该系统旨在利用基于搜索的机器学习算法(称为N-gram分析)激发基于静态代码分析的恶意软件检测,并检测移动应用程序中未被注意的恶意特征或漏洞。
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Detecting software vulnerabilities in android using static analysis
Now a day's mobile devices like Smartphone, tablets and Personal Digital Assistants etc. were playing most essential part in our daily lives. A high-end mobile device performs the same functionality as computers. Android based smart phone has become more vulnerable, because of an open source operating system. Anyone can develop a new application and post it into android market. These types of applications were not verified by authorized company. So it may include malevolent applications it may be virus, spyware, worms, etc. which can cause system failure, wasting memory resources, corrupting data, stealing personal information and also increases the maintenance cost. Due to these reasons, the mobile phone security or mobile security is very essential one in mobile computing. In the existing system is not able to detect new viruses, due to the limitation of updated signatures. The proposed system aims to motivate static code analysis based malware detection using search based machine learning algorithm which is called N-gram analysis and it detects the unnoticed malicious characteristics or vulnerabilities in the mobile applications.
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