Research on Static Detection Method of Android Malware Based on Combinatorial Features
Aiming at the problems that the current static detection methods of Android malware have a single type in feature selection,a large number of the same types and low efficiency of the detection model,this paper proposes a static detection method of Android malware based on combined features.The combined feature set consists of three aspects:permission,component and predictability.First,the features of differ-ent aspects are selected and retained as the final feature set by means of experiment and reasoning.Secondly,the classification rules of deci-sion tree nodes are optimized according to the information gain of feature attributes,and the detection model is constructed.The experimental results show that the detection accuracy and efficiency of the improved decision tree algorithm model are greatly improved,and the detection results are better than those of the popular random forest algorithm,support vector machine algorithm and naive Bayes algorithm under the same experimental environment.
malware detectioncombinatorial featuresstatic analysisfeature setdecision tree algorithm
万方数据
维普
