Detection of Android Malware using Recurrent neural network

With Android’s dominant position within the current smartphone OS, increasing number of malware applications pose a great threat to user privacy and security. Classification algorithms that use a single feature usually have weak detection performance. Although the use of multiple features can improve the detection effect, increasing the number of features increases the requirements of the operating environment and consumes more time. In existing system, a fast Android malware detection framework are preprocessed with the N-Gram technique and the FCBF (Fast Correlation-Based Filter) algorithm based on symmetrical uncertainty is employed to reduce feature dimensionality. Finally, the dimensionality reduced features are input into the CatBoost classifier for malware detection and family classification. In proposed system, the project is expected to show better results by implementing Recurrent Neural Networks (RNN)