/Malware-Detection

With the growth of mobile devices and applications, the number of malicious software, or malware, is rapidly increasing in recent years, which calls for the development of advanced and effective malware detection approaches. We propose an android malware detection system using a deep learning technique called graph convolutional neural networks. The network works on android applications. We extract some information about the application and add it to the windows event log of the server containing the network. This is the main server which is a part of a local area network that contains a number of computers that send their windows event logs to Elastic Stack. We then use the Stack to visualize this data on a browser to do some further analysis.

Primary LanguagePython

GP

Graduation Project This is my graduation project, under supervision of Dr. Khaled Fouad Elsayed and Dr. Husam Kinawi. And technically supported by Dr. Dina Salem.

The project briefly is a bout Android malware detection system using a deep learning technique called graph convolutional neural networks (GCN). The network works on android applications. Some information about the application is extracted and added to the windows event log of the server containing the network.

This is the main server which is a part of a local area network that contains a number of computers that send their windows event logs to Elastic Stack. The Elastic Stack is used to visualize this data on a browser to do some further analysis.