dentifying Zero Day Android Daily through Neural Networks

With the increase in the Internet's penetration rate in life and the use of this technology in all aspects, the use of mobile phones has increased as well. This, in addition to creating many benefits, has expanded and accelerated the release of some malicious programs called malware. In this study, it is attempted to use a multilayer neural network and learning machine diagnosis of zero daytime malware on smartphones. For this purpose, the standard database has been labeled with more than 15,000 samples of malware and goodware. In the pre -processing phase, the data is first performed using normalization and alignment of the data and by analyzing the main components of the feature of the selection of the feature and selected from 1183 features 215 features that have higher variances, followed by the model. A suggestion is introduced from the multilayer neural network class and the optimization algorithm based on the training and learning that apply it to the databases and compare its classification results with vector algorithms, genetic algorithm, nearest neighbor. And ... it can be seen that the neural network training increases accuracy and accuracy. The results of the use of multilayer neural network based on education and learning indicate 99% accuracy and 98% accuracy.