论文标题
物联网应用中的隐私泄漏标识的自动化方法
An Automated Approach for Privacy Leakage Identification in IoT Apps
论文作者
论文摘要
本文介绍了一种完全自动化的静态分析方法和一种工具，即污点，以识别智能物联网应用中的污染流。 Taint-Things准确地识别了一种最先进的工具，其性能提高了4倍。我们的方法报告了潜在的脆弱的污染流程，以简洁的安全切片的形式进行，其中代码的相关部分与影响敏感信息的线相关部分，这可以为安全审计师提供有效而精确的工具，以在测试中查明安全性问题。我们还提出并测试方法，通过增加额外的敏感性来增加污点的精度；我们通过可以添加到污染物的模块进行流动，路径和上下文敏感分析的不同方法。我们提出了通过在智能应用程序数据集上运行污染物，并在突变框架生成的集合上进行测试，以查看未添加误报而在不添加误报的情况下获得了多少覆盖范围，从而评估了污点。这表明了速度的提高，既可以提高4倍，又可以通过提供更高级别的流量和路径灵敏度分析（与最先进的工具之一）来提高精确度避免误报。
This paper presents a fully automated static analysis approach and a tool, Taint-Things, for the identification of tainted flows in SmartThings IoT apps. Taint-Things accurately identifies all tainted flows reported by one of the state-of-the-art tools with at least 4 times improved performance. Our approach reports potential vulnerable tainted flows in a form of a concise security slice, where the relevant parts of the code are given with the lines affecting the sensitive information, which could provide security auditors with an effective and precise tool to pinpoint security issues in SmartThings apps under test. We also present and test ways to add precision to Taint-Things by adding extra sensitivities; we provide different approaches for flow, path and context sensitive analyses through modules that can be added to Taint-Things. We present experiments to evaluate Taint-Things by running it on a SmartThings app dataset as well as testing for precision and recall on a set generated by a mutation framework to see how much coverage is achieved without adding false positives. This shows an improvement in performance both in terms of speed up to 4 folds, as well as improving the precision avoiding false positives by providing a higher level of flow and path sensitivity analysis in comparison with one of state of the art tools.
