基于统计学特征的android恶意应用检测方法

iming at the problem of ignoring the statistical significance of features in detection of Android malicious applications, an Android malicious application detection method based on statistical features was proposed. This method extracted the statistical characteristics of the training data set and used a clustering algorithm to preprocess the malicious data set for reducing the impact of individual differences on the experimental results. On the other hand, this method combined the features and various machine learning algorithms (such as linear regression, neural network, etc.) to establish a detection model. The accuracy rate of the two models proposed by this method could reach more than 95%, and the detection time could be greatly reduced compared with the comparison experiment. Experimental results show that the statistical characteristics of the application can be used to distinguish between benign and malicious applications, and preprocessing the data by clustering algorithm can improve the detection accuracy.