Abstract
In recent years, with the quiet popularity of mobile payment methods, mobile terminal equipment also have potential security problems while facilitating people’s lives. Behavior-based Android malware detection is mostly based on permission analysis and API calls. In this paper, we propose a static Android malicious detection scheme based on sensitive API calls. We extracted all APIs called in the experimental samples through decompilation, and then calculated and ranked the threats related to these APIs according to the mutual information model, selected the top 20 sensitive API calls, and generated a 20-dimensional feature vector for each application. In the classification process, an integrated learning model based on DT classifier, kNN classifier and SVM classifier is used to effectively detect unknown APK samples. We collected 516 benign samples and 528 malicious samples. Through a large number of experiments, the results show that the accuracy of our scheme can be up to 94%, and the precision is up to 95%.
Supported by Tianjin Nature Science Youth Foundation (No.18JCQNJC69900).
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Yu, J., Zhao, C., Zheng, W., Li, Y., Zhang, C., Chen, C. (2021). Android Malware Detection Using Ensemble Learning on Sensitive APIs. In: Jiang, H., Wu, H., Zeng, F. (eds) Edge Computing and IoT: Systems, Management and Security. ICECI 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 368. Springer, Cham. https://doi.org/10.1007/978-3-030-73429-9_8
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DOI: https://doi.org/10.1007/978-3-030-73429-9_8
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