DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model

@article{Zhu2018DroidDetEA,
  title={DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model},
  author={Hui-Juan Zhu and Zhu-Hong You and Zexuan Zhu and Wei-Lei Shi and Xing Chen and Li Cheng},
  journal={Neurocomputing},
  year={2018},
  volume={272},
  pages={638-646}
}
The Android platform is becoming increasingly popular and various organizations have developed a variety of applications (App) to cater to market trends. Due to the characteristics of the Android platform, such as supporting the unofficial App stores, open source policy and the great tolerance for App verification, it is inevitable that it faces serious problems of malicious software intrusion. In order to protect the users from the serious damages caused by Android malware, we propose a low… CONTINUE READING
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  • The experimental results show that the proposed method achieves an high accuracy of 88.26% with 88.40% sensitivity at the precision of 88.16%.

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