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Adversarial Samples on Android Malware Detection Systems for IoT Systems.

Xiaolei Liu1, Xiaojiang Du2, Xiaosong Zhang3

  • 1School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China. liuxiaolei@uestc.edu.cn.

Sensors (Basel, Switzerland)
|March 3, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel testing framework (TLAMD) to generate adversarial samples for Android malware detection in IoT devices. The framework achieves nearly 100% success in black-box testing without needing model parameters.

Failed At:

2026-06-19T13:38:13.396259+00:00

Keywords:
Internet of Thingsadversarial samplesmachine learningmalware detection

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