Iota：一个分析IoTs系统级安全性的框架
Iota: A Framework for Analyzing System-Level Security of IoTs
摘要：
大多数物联网系统涉及物联网设备、通信协议、远程云、物联网应用程序、移动应用程序和物理环境。然而，现有的物联网安全分析只关注所有必要组件的子集，如设备固件，而忽略了物联网系统的交互性，导致攻击检测能力有限。在这项工作中，我们提出了Iota，一个基于逻辑编程的框架来执行物联网系统的系统级安全分析。Iota为物联网系统生成攻击图，显示所有可能受到攻击的系统资源，并列举潜在的攻击痕迹。在构建Iota的过程中，我们设计了新颖的技术来扫描物联网系统中的单个漏洞，并进一步为物联网漏洞创建通用的利用模型。我们还识别和建模不同设备之间的物理依赖关系，因为它们是物联网系统所独有的，并且被对手用来发起复杂的攻击。此外，我们利用NLP技术来提取基于应用描述的物联网应用语义。为了评估漏洞对系统范围的影响，我们根据攻击图提出了两个指标，为加强物联网系统提供指导。通过对127个物联网漏洞的评估表明，Iota的漏洞利用建模模块在预测漏洞的前提条件和影响方面达到了80%以上的准确率。我们将Iota应用于37个基于真实物联网应用和设备的合成灵巧家庭物联网系统。实验结果表明，我们的框架是有效的和高效的。在攻击图显示的27条最短攻击路径中，62.8%是系统管理员没有预料到的。只需1.2秒即可生成和分析由50台设备组成的物联网系统的攻击图。
英文摘要：
Most IoT systems involve IoT devices, communication protocols, remote cloud, IoT applications, mobile apps, and the physical environment. However, existing IoT security analyses only focus on a subset of all the essential components, such as device firmware, and ignore IoT systems' interactive nature, resulting in limited attack detection capabilities. In this work, we propose Iota, a logic programming-based framework to perform system-level security analysis for IoT systems. Iota generates attack graphs for IoT systems, showing all of the system resources that can be compromised and enumerating potential attack traces. In building Iota, we design novel techniques to scan IoT systems for individual vulnerabilities and further create generic exploit models for IoT vulnerabilities. We also identify and model physical dependencies between different devices as they are unique to IoT systems and are employed by adversaries to launch complicated attacks. In addition, we utilize NLP techniques to extract IoT app semantics based on app descriptions. To evaluate vulnerabilities' system-wide impact, we propose two metrics based on the attack graph, which provide guidance on fortifying IoT systems. Evaluation on 127 IoT CVEs (Common Vulnerabilities and Exposures) shows that Iota's exploit modeling module achieves over 80% accuracy in predicting vulnerabilities' preconditions and effects. We apply Iota to 37 synthetic smart home IoT systems based on real-world IoT apps and devices. Experimental results show that our framework is effective and highly efficient. Among 27 shortest attack traces revealed by the attack graphs, 62.8% are not anticipated by the system administrator. It only takes 1.2 seconds to generate and analyze the attack graph for an IoT system consisting of 50 devices.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
