Automatic Modulation Recognition of Communication Signals
Automatic Modulation Recognition of Communication Signals
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Detecting Spoofing and Anomalous Traffic in Wireless Networks via Forge-Resistant Relationships
IEEE Transactions on Information Forensics and Security
Improved wireless security for GMSK-based devices using RF fingerprinting
International Journal of Electronic Security and Digital Forensics
Sensitivity analysis of burst detection and RF fingerprinting classification performance
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Using differential evolution to optimize 'learning from signals' and enhance network security
Proceedings of the 13th annual conference on Genetic and evolutionary computation
On physical-layer identification of wireless devices
ACM Computing Surveys (CSUR)
Analysis of impersonation attacks on systems using RF fingerprinting and low-end receivers
Journal of Computer and System Sciences
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There is a current shift toward protecting against unauthorisednetwork access at the open systems interconnection physical layerby exploiting radio frequency characteristics that are difficult tomimic. This work addresses the use of RF 'fingerprints' to uniquelyidentify emissions from commercial devices. The goal is to exploitinherent signal features using a four step process that includes:1. feature generation, 2. transient detection, 3. fingerprintextraction and 4. classification. Reliable transient detection isperhaps the most important step and is addressed here using avariance trajectory approach. Following transient detection, twofingerprinting and classification methods are considered, including1. power spectral density (PSD) fingerprints with spectralcorrelation and 2. statistical fingerprints with multiplediscriminant analysis-maximum likelihood (MDA-ML) classification.Each of these methods is evaluated using the 802.11a orthogonalfrequency-division multiplexing (OFDM) signal. For minimaltransient detection error, results show that amplitude-baseddetection is most effective for 802.11a OFDM signals. It is shownthat MDA-ML classification provides approximately 8.5-9.0% betterclassification performance than spectral correlation over a rangeof analysis signal-to-noise ratios (SNRA) using threehardware devices from two manufacturers. Overall, greater than 80%classification accuracy is achieved for spectral correlation atSNRA 6 dB and for MDA-ML classification atSNRA -3 dB.