The detection system then relies on Convolution Neural Networks (CNN) to determine whether the data gathered indicates the presence of a threat. Using this technique, researchers claims they could record 100,000 measurement traces from IoT devices infected by genuine malware samples, and predicted three generic and one benign malware class with an accuracy as high as 99.82%. Best of all, no software is needed and the device you’re scanning doesn’t need to be manipulated in any way. As such, bad actors won’t be successful with their attempts to conceal malicious code from malware detection software using obfuscation techniques. “Our method does not require any modification on the target device. Thus, it can be deployed independently from the resources available without any overhead. Moreover, our approach has the advantage that it can hardly be detected and evaded by the malware authors,” researchers wrote in the paper.
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