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Non-invasive Privilege Escalation through Mobile and IoT System Interface: Threats and Mitigation

机译:通过移动和物联网系统接口的非侵入式特权升级:威胁和缓解

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摘要

With the proliferation of mobile and IoT devices, malicious application developers seize the opportunity to spread malicious applications threatening the security and privacy of users' information assets. In this dissertation, we work towards understanding and mitigating a unique type of threat, non-invasive privilege escalation attacks, posed by malicious applications to vulnerable mobile and IoT system interfaces. Unlike more invasive attacks that usually gain elevated access through altering the memory or files belonging to the system or other applications, a non-invasive attack leverages legitimate yet vulnerable system interfaces to gain access to system resources, other application resources or to infer sensitive user information, which is usually difficult to detect without in-depth understanding of the vulnerable systems.;In particular, this dissertation reports a systematic study on this understudied type of threat, from the hidden weaknesses inside the operating system, to the risks introduced by the mobile ecosystem and to a new user-computing interfaces. Specifically, we studied a runtime-information-gathering (RIG) threat which exploits design weaknesses of the operating system, e.g., shared communication channels such as Bluetooth, and side channels such as memory and network-data usages, on Android and Android-based IoT devices. To defend against this new category of attacks, we propose a novel approach, App Guardian, that changes neither the operating system nor the target apps, and provides immediate protection as soon as an ordinary app is installed. Our experimental studies show that this new technique defeated all known RIG attacks, with small impacts on the utility of legitimate apps and the performance of the operating system.;Then we discover hanging attribute references (Hares), a type of vulnerabilities never investigated before, which often have serious security implications: when an attribute is used on a device but the party defining it has been removed during vendor customization, a malicious app can fill the gap to acquire critical system capabilities, by simply disguising as the owner of the attribute. We further design and implement Harehunter , a new tool for automatic detection of Hares. By using it, we discover 21,557 likely Hare flaws on the factory images of 97 most popular Android devices, demonstrating the significant impacts of the problem.;Finally, we conduct the first security analysis on Voice Personal Assistant (VPA) ecosystems and related popular IoT devices, which leads to the discovery of serious security weaknesses in their Voice User Interfaces (VUIs) and skill vetting. We present two new attacks, voice squatting and voice masquerading, both of which are demonstrated to pose realistic threats to a large number of VPA users from remote and both have serious security and privacy implications. We also design and implement new techniques that make the first step towards protecting VPA users from these voice-based attacks.
机译:随着移动和物联网设备的激增,恶意应用程序开发人员抓住了传播恶意应用程序的机会,从而威胁到用户信息资产的安全性和隐私性。在本文中,我们致力于理解和缓解由恶意应用程序对易受攻击的移动设备和物联网系统接口造成的独特类型的威胁,非侵入性特权升级攻击。与通常通过更改属于系统或其他应用程序的内存或文件来获得更高访问权限的更具侵入性的攻击不同,非侵入性攻击利用合法但易受攻击的系统接口来获取对系统资源,其他应用程序资源的访问或推断敏感的用户信息;如果不深入了解易受攻击的系统,通常很难检测到该漏洞。尤其是,本论文针对这种未被充分研究的威胁进行了系统的研究,从操作系统内部的隐患到移动设备带来的风险。生态系统和新的用户计算界面。具体来说,我们研究了运行时信息收集(RIG)威胁,该威胁利用了操作系统的设计弱点,例如,基于Android和Android的共享通信通道(如蓝牙)和辅助通道(如内存和网络数据使用情况)物联网设备。为了防御这种新型攻击,我们提出了一种新颖的方法,即App Guardian,该方法既不更改操作系统也不更改目标应用程序,并在安装了普通应用程序后立即提供保护。我们的实验研究表明,这项新技术击败了所有已知的RIG攻击,对合法应用的实用性和操作系统的性能影响不大。然后,我们发现了悬挂属性引用(Hares),这是一种以前从未研究过的漏洞,这通常具有严重的安全隐患:当在设备上使用属性但在供应商自定义期间删除了定义属性的一方时,恶意应用程序可以简单地伪装为属性的所有者,从而填补获取关键系统功能的空白。我们进一步设计并实现了Harehunter,这是一种自动检测野兔的新工具。通过使用它,我们在97种最流行的Android设备的工厂映像中发现了21557个可能的Hare漏洞,证明了问题的严重影响。最后,我们对语音个人助理(VPA)生态系统和相关的流行物联网进行了首次安全分析设备,这会导致语音用户界面(VUI)和技能审查中发现严重的安全漏洞。我们提出了两种新的攻击方式,即语音下蹲和语音伪装,这两种攻击都被证明对来自远程的大量VPA用户构成了现实威胁,并且都具有严重的安全性和隐私隐患。我们还设计和实施新技术,这是迈向保护VPA用户免受这些基于语音的攻击的第一步。

著录项

  • 作者

    Zhang, Nan.;

  • 作者单位

    Indiana University.;

  • 授予单位 Indiana University.;
  • 学科 Computer science.
  • 学位 Ph.D.
  • 年度 2018
  • 页码 165 p.
  • 总页数 165
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:52:57

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