基于随机森林算法的Android恶意行为识别与分类方法

An Android malware behavior identification and classification method was proposed based on random forest (RF) algorithm aiming at the problem that the existing Android malware detection method cannot identify or classify the detected malicious behavior. The types of Android malware behavior were defined, and the potentially malicious behavior was triggered with a complex Android malicious behavior induction method. Application behavior can be captured by system function hook and transformed into behavior log. Then application behavioral feature set can be extracted from behavior log. The random forest algorithm was used to identify and classify the malicious behavior from the behavior log. The experimental results showed that proposed method had 91.6% accuracy in malware behavior identification and 96.8% accuracy in malicious behavior classification.