The world’s largest companies are battling increasingly widespread and sophisticated malware attacks, but an exciting new malware detection strategy can help companies thwart these threats without the need for software.
A team of researchers at the French Research Institute of Computer Science and Random Systems has developed an anti-malware system centered on raspberry pie that scans devices for electromagnetic waves. As reported Tom’s hardware, The safety device uses an oscilloscope (Picoscope 6407) attached to a Raspberry Pi 2B and H-field probes that sort out the abnormalities of specific electromagnetic waves emitted by computers under attack, a technique that researchers say is used to gain precise knowledge. About type and identity. “
The detection system then relies on the Convolution Neural Network (CNN) to determine if the data collected indicates the presence of a threat. Using this technique, the researchers claimed that they could record 100,000 measurement traces from IoT devices infected by actual malware samples and predicted three generic and one benign malware class with 99.82% accuracy.
After all, no software is required and there is no need to manipulate the device you are scanning in any way. As such, bad actors will not succeed in trying to hide malicious code from malware detection software using obscurity techniques.
“Our system does not require any changes to the target device. Thus, it can be deployed independently of available resources without any overhead. Furthermore, the advantage of our approach is that it is rarely detected and avoided by malware authors, “said the researchers. Wrote on paper.
Keep in mind that this system was created for research purposes, not to be published as a commercial product, although it can inspire security teams to look at innovative ways to use EM waves to detect malware. The research is currently at an early stage and the neural network needs to be further trained before any practical use can be made of it.
For now, the system is a unique way to protect devices, making it difficult for malware writers to hide their code, but the technology is not available to the public.
And while using a raspberry pie may seem promising as a low-cost way to detect malware, other EM wave-scanning tools can cost up to a few thousand dollars. Despite its limitations, it encourages us to look at such an important issue of research methodology from a unique angle – who knows, this simple setup can help prevent major breaches one day after another.