  Many IoT(Internet of Things) systems run Android systems or Android-like
systems. With the continuous development of machine learning algorithms, the
learning-based Android malware detection system for IoT devices has gradually
increased. However, these learning-based detection models are often vulnerable
to adversarial samples. An automated testing framework is needed to help these
learning-based malware detection systems for IoT devices perform security
analysis. The current methods of generating adversarial samples mostly require
training parameters of models and most of the methods are aimed at image data.
To solve this problem, we propose a \textbf{t}esting framework for
\textbf{l}earning-based \textbf{A}ndroid \textbf{m}alware \textbf{d}etection
systems(TLAMD) for IoT Devices. The key challenge is how to construct a
suitable fitness function to generate an effective adversarial sample without
affecting the features of the application. By introducing genetic algorithms
and some technical improvements, our test framework can generate adversarial
samples for the IoT Android Application with a success rate of nearly 100\% and
can perform black-box testing on the system.
