Protocol Switching Mechanism for Countering Radiometric Signature Exploitation

Manufacturing variations introduce features that distinguish radio transceivers even those of the same vendor. Such distinction is often referred to as radiometric signature and is found to be useful in conducting device authentication and crime forensics. Yet, radiometric signatures could constitute a privacy threat. Particularly, in the realm of wireless networks, an adversary may exploit RF fingerprinting to identify devices and conduct traffic analysis in order to uncover the topology and categorize the role of various nodes. In this paper, we show that RF fingerprinting could be a major tool for the adversary to distinguish among nodes and bypass the provisioned anonymity protection in the network. We analyze the accuracy of RF fingerprinting and highlight how the accuracy affects the success of adversary attacks. We further develop a novel countermeasure to degrade the adversary's ability in exploiting RF fingerprinting. The proposed countermeasure is based on switching among preset communication protocols and employs adversarial machine learning to select the protocol for a transmission so that the accuracy of the RF fingerprinting diminishes. We demonstrate the effectiveness of our scheme through simulation and prototype experiments.