Carnegie Mellon University (CMU) researchers strengthened the security of Internet of Things (IoT) devices by making them more resilient against exploitation through their development of radio -requency fingerprinting (RFF).
RFF can be used to identify specific IoT devices by detecting hardware variations that produce unique radio wave signatures.
CMU's Jiachen Xu used power amplifiers to foil RFF exploits by changing the IoT signal's features, and a convolutional neural network classified incoming signals as safe or unsafe by assessing the RFF in the processed signal.
The researchers also proved Bayesian neural networks could identify and classify RFF quickly and accurately, without requiring excessive computational power.
From Carnegie Mellon University College of Engineering News
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