Detecting rogue devices in bluetooth networks using radio frequency fingerprinting

Unauthorized Bluetooth devices or rogue devices can impersonate legitimate devices through address and link key spoofing. Moreover, they can infiltrate a Bluetooth network and initiate other forms of attacks. This paper investigates a novel intrusion detection approach, which makes use of radio frequency fingerprinting (RFF) for profiling, Hotelling’s T 2 statistics for classification and a decision filter, for detecting these devices. RFF is a technique that is used to uniquely identify a transceiver based on the transient portion of the signal it generates. Moreover, the use of a statistical classifier proves advantageous in minimizing requirements for memory. Finally, the detection rate is also improved by incorporating a decision filter, which takes the classification results of a set of events into consideration, prior to rendering the final decision. The average False Alarm Rate of five percent and Detection Rate of ninety-three percent support the feasibility of employing these components to address the aforementioned problem.