Raspberry Pi, the versatile single board microcomputer with which we can create hundreds of projects to automatize our house, create an alarm or play retro games among many other functionalities, has been the protagonist of an investigation in which they have used this board to create a new malware detection system without the need for any additional software.
The Raspberry Pi is an affordable microcomputer that fits in the palm of our hand that allows us all kinds of uses, as many as our imagination reaches. For example, it is enough to connect peripherals or a screen to make it perform the functions of a computer, but it can be used for all kinds of projects regardless of the model we have. On the Internet we can find countless tutorials with which to carry out our first projects and also studies in which new uses that can be given to the Raspberry Pi are discovered, such as the one that we present below.
As a malware detection system
A team of researchers from the Research Institute for Computer Science and Random Systems (IRISA) in France, made up of Annelie Heuser, Matthieu Mastio, Duy-Phuc Pham and Damien Marion have managed to create a new virus detection system without the need for have additional software.
Unlike other software-dependent virus detection systems, “IRISA’s new system uses an oscilloscope (Picoscope 6407) and an H-Field probe in addition to a Raspberry Pi 2B to scan devices for electromagnetic (EM) waves. specific”.
through electromagnetic waves
IRISA, as it has called the new system, “is based on an external oscilloscope and an H-Field probe to scan devices looking for specific waves that indicate the presence of malware on an affected device.” However, the team of researchers has also managed to obtain precise information about the type of identity of the malware as a result of such scans.
By not using additional software, many of the techniques used by cybercriminals and hackers are completely ineffective and are easily detected and analyzed by the system. Although the Raspberry Pi used in this project was tested with safe and malicious data sets, the researchers also used Convolution Neural Networks (CNN) to assess the data for threats.
The IRISA malware detection system had an effectiveness of up to 99.82% in the tests carried out. However, this virus detection system is far from being commercially implemented, but it could make things very difficult for malware creators, as they will now have to find a way to hide malicious code from electromagnetic wave scanners.
