  It is estimated that the number of IoT devices will reach 75 billion in the
next five years. Most of those currently, and to be deployed, lack sufficient
security to protect themselves and their networks from attack by malicious IoT
devices that masquerade as authorized devices to circumvent digital
authentication approaches. This work presents a PHY layer IoT authentication
approach capable of addressing this critical security need through the use of
feature reduced Radio Frequency-Distinct Native Attributes (RF-DNA)
fingerprints and Support Vector Machines (SVM). This work successfully
demonstrates 100%: (i) authorized ID verification across three trials of six
randomly chosen radios at signal-to-noise ratios greater than or equal to 6 dB,
and (ii) rejection of all rogue radio ID spoofing attacks at signal-to-noise
ratios greater than or equal to 3 dB using RF-DNA fingerprints whose features
are selected using the Relief-F algorithm.
