Research on Static and Dynamic Detection Method of Mobile Application Security Based on Deep Learning
The third-party libraries and SDKS that many Android,iOS,and Android mobile op-erating system applications rely on are frequently updated,and new versions fix security vulner-abilities in older versions,but also introduce new ones.The application involved in each plat-form system is complex and dynamic,and its code base is relatively large,which is difficult to cover the risk detection points of each platform.Therefore,the static and dynamic detection method of mobile application security based on deep learning is proposed.The combined method of weighted average filtering and Gaussian filtering is used to filter the traffic data of mobile applications and input it into the deep learning TensorFlow framework to extract the amplitude characteristics of traffic fluctuations and calculate the matching degree between the obtained am-plitude characteristics of traffic fluctuations and the static and dynamic detection standard charac-teristics,so as to realize the static and dynamic detection of the safe operation of mobile applica-tions.The experimental results show that the method has higher recall rate,more security vul-nerabilities can be detected,and less response time under different data volumes.
Deep learningMobile applicationsSafety testingFeature extraction
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