A Mutation Framework for Evaluating Security Analysis tools in IoT Applications
This work addresses the need for better security evaluation tools in IoT applications, specifically for SmartThings, but is incremental as it builds on existing mutation testing concepts.
The paper tackled the problem of evaluating security analysis tools for IoT applications by presenting an automated mutation framework to assess taint-flow analyzers, resulting in rankings based on precision and recall, such as Taint-Things achieving 99% recall and 100% precision.
With the growing and widespread use of Internet of Things (IoT) in our daily life, its security is becoming more crucial. To ensure information security, we require better security analysis tools for IoT applications. Hence, this paper presents an automated framework to evaluate taint-flow analysis tools in the domain of IoT applications. First, we propose a set of mutational operators tailored to evaluate three types of sensitivity analysis, flow, path and context sensitivity. Then we developed mutators to automatically generate mutants for those types. We demonstrated the framework on a subset of mutational operators to evaluate three taint-flow analyzers, SaINT, Taint-Things and FlowsMiner. Our framework and experiments ranked the taint analysis tools according to precision and recall as follows: Taint-Things (99% Recall, 100% Precision), FlowsMiner (100% Recall, 87.6% Precision), and SaINT (100% Recall, 56.8% Precision). To the best of our knowledge, our framework is the first framework to address the need for evaluating taint-flow analysis tools and specifically those developed for IoT SmartThings applications.