AppCaulk: Data Leak Prevention by Injecting Targeted Taint Tracking into Android Apps

3997
    


来源:
Licence:
联系:
分类:
平台:
环境:
大小:
更新:
标签:
联系方式 :
免费下载 ×

下载APP,支持永久资源免费下载

限免产品服务请联系qq:1585269081

下载APP
免费下载 ×

下载APP,支持永久资源免费下载

下载APP 免费下载
下载 ×

下载APP,资源永久免费


如果出现不能下载的情况,请联系站长,联系方式在下方。

免费下载 ×

下载论文助手APP,资源永久免费

免费获取

如果你已经登录仍然出现不能下载的情况,请【点击刷新】本页面或者联系站长


As Android is entering the business domain, leaks of business-critical and personal information through apps become major threats. Due to the context-insensitive nature of the Android permission model, information flow policies cannot be enforced by on-board mechanisms. We therefore propose AppCaulk, an approach to harden any existing Android app by injecting a targeted dynamic taint analysis, which tracks and blocks unwanted information flows at runtime. Critical data flows are first discovered using a static taint analysis and the relevant data propagation paths are instrumented by a taint tracking code at register level. At runtime the dynamic taint analysis woven into the app detects and blocks data leaks as they are about to occur. In contrast to existing taint analysis approaches like Taint droid, AppCaulk does not require modification of the Android middleware and can thus be applied to any stock Android installation. In this paper, we explain the design of AppCaulk, describe the evaluation of its prototype, and compare its effectiveness with Taint droid.

参考文献


免费下载 ×

下载APP,支持永久资源免费下载

下载APP 免费下载
温馨提示
请用电脑打开本网页,即可以免费获取你想要的了。
扫描加我微信 ×

演示

×
登录 ×


下载 ×
论文助手网
论文助手,最开放的学术期刊平台
				暂无来源信息			 
回复
来来来,吐槽点啥吧

作者联系方式

×

向作者索要->