Many IoT (Internet of Things) systems run Android systems or Android-like systems.\nWith the continuous development of machine learning algorithms, the learning-based Android\nmalware detection system for IoT devices has gradually increased. However, these learning-based\ndetection models are often vulnerable to adversarial samples. An automated testing framework is\nneeded to help these learning-based malware detection systems for IoT devices perform security\nanalysis. The current methods of generating adversarial samples mostly require training parameters\nof models and most of the methods are aimed at image data. To solve this problem, we propose a\ntesting framework for learning-based Android malware detection systems (TLAMD) for IoT Devices.\nThe key challenge is how to construct a suitable fitness function to generate an effective adversarial\nsample without affecting the features of the application. By introducing genetic algorithms and some\ntechnical improvements, our test framework can generate adversarial samples for the IoT Android\napplication with a success rate of nearly 100% and can perform black-box testing on the system.
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