Android malware network traffic detection using visual representation (AF)
There is a growing concern among mobile device users worldwide about Android malware. An effective method of detecting and analyzing Android malware is through the analysis of the network traffic that is generated because of the malware's operation. A malware analysis can provide valuable insights into the inner workings of the malware, as well as its intended targets and potential impacts to the user. Our goal in this paper is to present a novel approach for detecting Android malware network traffic using visual representation techniques to detect Android malware network traffic. As part of our approach, we utilize advanced data visualization techniques to display network traffic data in a clear, effective, and efficient manner, allowing for the efficient and accurate analysis of Android malware network activity. In our presentation, we will present the developer tool, which is a script that allows users to automate Frida commands and easily retrieve information regarding an app's classes, properties, and methods. It will also provide users with a script tool which utilizes the Android Debug Bridge (adb) command-line tool to perform a variety of actions related to Android applications. It is our belief that our approach will be a valuable tool for those researchers and analysts who are working to prevent Android malware from spreading throughout the world.