A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms

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Abstract
Android malwares severely threaten system and user security in terms of privilege escalation, remote control, tariff theft, and privacy leakage. Therefore, it is of great importance and necessity to detect Android malwares. In this paper, we present a combination method for Android malware detection based on the machine learning algorithm. First, we construct the control flow graph of the application to obtain API information. Based on the API information, we innovatively constructs boolean, frequency, and time-series data sets. Based on these three data sets, three detection models for Android malware detection regarding API calling, API frequency and API sequence aspects are constructed. Ultimately, an ensemble model is constructed for conformity. We tested and compared the accuracy and stability of our detection models through a large number of experiments. The experiments were conducted on 10010 benign applications and 10683 malicious applications. The results show that our detection model achieves 98.98% detection precision and has high accuracy and stability. All of the results are consistent with the theoretical analysis in this paper.
Funding Information
  • National Natural Science Foundation of China (U1764263, 61872283)
  • Natural Science Basic Research Plan in the Shaanxi Province of China (2016JM6074)
  • China 111 Project (B16037)