Research on dynamic detection of Android malware based on mixed features and deep learning
To avoid privacy breaches,a dynamic detection method for Android malware based on mixed features and deep learn-ing is studied to achieve the efficiency and accuracy of Android malware dynamic detection.Prevent malicious Android software from detecting simulated environment processes through anti detection schemes,and run the tested Android software in the simulator.Col-lect dynamic running data of the Android software,and preprocess the Android software running data file through decompression and decompilation.Extract Android malware mixed features composed of function call graph features,byte probability features,and APK permission features from the preprocessed Android software file,Use the obtained mixed features of Android malware as input data for improving the self coding network,and output the dynamic detection results of whether the Android software is normal or malicious.The experiment shows that this method can achieve dynamic detection of Android malware and obtain the type of malware,with a short dynamic detection time and good evaluation index values for Android malware dynamic detection.
万方数据
维普
