A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices

Abstract

Concerns about the threat of piracy and the spread of viruses have been raised as a result of the rise in popularity of Android applications. Through applications, malware is frequently propagated in the mobile environment. People frequently grant applications rights without fully understanding their purpose. Android devices are used by almost 2.8 billion people worldwide. Therefore, the requirement for a reliable Android malware detection system is unavoidable. In order to provide a real-time and responsive detection environment for Android mobile devices, this paper proposes MobiTive, an efficient Android malware detection system. A more practical and secure application solution is MobiTive, which checks and monitors APK files before installation. MobiTive is concentrating on mobile and server-side platforms. APK files are mined for API calls and Manifest files of both malware and benign apps, and this raw data is then fed into convolutional neural networks. After being trained independently, networks including GRU, BI-GRU, STACKED GRU, and LSTM were shown to be of good performance. Its shown that BI-GRU have the highest performance. As quantization is done for mobile device adaptation, network model size varies. Deep Learning model must be converted to Tensorflow-lite model due to limited computing power and resource availability in order to function well on the mobile platform. MobiTive has been evaluated for effectiveness on a variety of mobile devices, and it achieves detection accuracy of 90.26% percent.