Using RF-DNA Fingerprints to Discriminate ZigBee Devices in an Operational Environment.

摘要

This research was performed to expand AFIT's Radio Frequency 'Distinct Native Attribute' (RF-DNA) fingerprinting process to support IEEE 802.15.4 ZigBee communication network applications. Current ZigBee bit-level security measures include use of network keys and MAC lists which can be subverted through interception and spoofing using open-source hacking tools. This work addresses device discrimination using Physical (PHY) waveform alternatives to augment existing bit-level security mechanisms. ZigBee network vulnerability to outsider threats was assessed using Receiver Operating Characteristic (ROC) curves to characterize both Authorized Device ID Verification performance (granting network access to authorized users presenting true bit-level credentials) and Rogue Device Rejection performance (denying network access to unauthorized rogue devices presenting false bit- level credentials). Radio Frequency 'Distinct Native Attribute' (RF-DNA) features are extracted from time-domain waveform responses of 2.4 GHz CC2420 ZigBee transceivers to enable humanlike device discrimination. The fingerprints were constructed using a 'hybrid' pool of emissions collected under a range of conditions, including anechoic chamber and an indoor office environment where dynamic multi-path and signal degradation factors were present. The RF-DNA fingerprints were input to a Multiple Discriminant Analysis, Maximum Likelihood (MDA/ML) discrimination process and a 1 vs. many 'Looks most like' classification assessment made. The hybrid MDA model was also used for 1 vs. 1 'Looks how much like' verification assessment. ZigBee Device Classification performance was assessed using both full and reduced dimensional fingerprint sets. Reduced dimensional subsets were selected using Dimensional Reduction Analysis (DRA) by rank ordering (1) pre-classification KS-Test p-values and (2) post-classification GRLVQI feature relevance values. Assessment of Zigbee device ID verification capability.