|Student Name:||Mr. Donald R. Reising|
|Thesis:||Exploitation of RF-DNA for Device Classification and Verification Using GRLVQI Processing|
|Location:||Building 640, Room 353 (ENY Conference Room)|
|Date & Time:||11/30/2012 at 0900|
|Abstract:|| This work presents a GRLVQI classification process and extends applicability of RF-DNA fingerprinting for device classification and ID verification. Unlike previous MDA/ML-based RF-DNA work, GRLVQI provides a measure of feature relevance that enables Dimensional Reduction Analysis (DRA) to enhance the experimental-to-operational transition potential of RF-DNA fingerprinting. Using 2D Gabor Transform RF-DNA fingerprints extracted from experimentally collected OFDM-based 802.16 WiMAX and 802.11 WiFi device emissions, average GRLVQI classification accuracy of %C>90% using full-dimensional features was achieved at SNR>10.0 dB and SNR>12.0 dB, respectively. Performance with DRA 90% reduced feature sets included %C>90% using 1) WiMAX features at SNR>12.0 dB and 2) WiFi features at SNR>13.0 dB. For device ID verification with DRA 90% feature sets, GRLVQI enabled: 1) 100% ID verification of authorized WiMAX devices and 97% detection of spoofing attacks by rogue devices at SNR=18.0 dB, and 2) 100% ID verification of authorized WiFi devices at SNR=15.0 dB.