基于静态行为特征的细粒度Android恶意软件分类.

Due to the openness of Android system, malware poses a threat to users of Android devices by implementing various malicious behaviors. At present, most of the existing researches focus on coarse-grained malicious detection, that is, whether an Android application is malicious or not. Aiming at this problem, this paper proposed a fine-grained malicious behavior classification method based on static behavior features. This method extracted and optimized multi-dimensional behavior characteristics, including API calls, permissions, intents and package dependencies. And then used random forest to classify malicious behaviors. It conducted the experiments on 24 553 malicious Android application samples in 73 malware families from multiple application markets. The experimental results show that the accuracy of fine-grained malware classification reached to 95. 88%, which is better than other comparison methods. [ABSTRACT FROM AUTHOR]