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.
