Commercial keyloggers supporting numerous functions and protected against detection may cost dozens and even hundreds of dollars. However, it is not that difficult to create a homemade keylogger and avoid antivirus alerts. In this article, I will explain how to do this; concurrently, we will practice our C# coding skills.
Since such devices as bladeRF, HackRF, RTL-SDR, and software systems like GNU Radio had become widely available, reverse engineering of radio air data got really simple and entertaining.
Everyday, new vulnerabilities are discovered in mobile devices that can be exploited by intruders. They can send an SMS to a pay-per-call number, they can collect and sell a large database of contact details, and they can also compromise a specific individual. Successful exploitation of a vulnerability requires that a whole range of conditions are met. There is another way, however! Provide the user with a really useful application (a game with birds), whose manifest contains a list of device information that we are interested in. In this article, we will look at ways of obtaining and saving important information from an Android device.
Everyone cares about their significant others' security. We all know that feeling when your calls are not answered and your Whatsapp messages not marked as read. In a moment like that you would do a lot to have any idea what is happening there. Although cell phone carriers offer geolocation services to locate another user, knowing your girlfriend is somewhere in the middle of Main Street will barely help. So what can we do about it?
At first, GPUs could be used for a very narrow range of tasks (try to guess what), but they looked very attractive, and software developers decided to use their power for allocating a part of computing to graphics accelerators. Since GPU cannot be used in the same way as CPU, this required new tools that did not take long to appear. This is how originated CUDA, OpenCL and DirectCompute. The new wave was named ‘GPGPU’ (General-purpose graphics processing units) to designate the technique of using GPU for general purpose computing. As a result, people began to use a number of completely different microprocessors to solve some very common tasks. This gave rise to the term “heterogeneous parallelism”, which is actually the topic of our today’s discussion.