Radio frequency fingerprinting commercial communication devices to enhance electronic security
William Suski, Michael A. Temple, Michael J. Mendenhall, Robert F. Mills · International Journal of Electronic Security and Digital Forensics · 2008
There is a current shift toward protecting against unauthorised network access at the open systems interconnection physical layer by exploiting radio frequency characteristics that are difficult to mimic. This work addresses the use of RF 'fingerprints' to uniquely identify emissions from commercial devices. The goal is to exploit inherent signal features using a four step process that includes: 1. feature generation, 2. transient detection, 3. fingerprint extraction and 4. classification. Reliable transient detection is perhaps the most important step and is addressed here using a variance trajectory approach. Following transient detection, two fingerprinting and classification methods are considered, including 1. power spectral density (PSD) fingerprints with spectral correlation and 2. statistical fingerprints with multiple discriminant analysis-maximum likelihood (MDA-ML) classification. Each of these methods is evaluated using the 802.11a orthogonal frequency-division multiplexing (OFDM) signal. For minimal transient detection error, results show that amplitude-based detection is most effective for 802.11a OFDM signals. It is shown that MDA-ML classification provides approximately 8.5-9.0% better classification performance than spectral correlation over a range of analysis signal-to-noise ratios (SNRA) using three hardware devices from two manufacturers. Overall, greater than 80% classification accuracy is achieved for spectral correlation at SNRA > 6 dB and for MDA-ML classification at SNRA > −3 dB.