Improving Intra-Cellular Security Using Air Monitoring with RF Fingerprints

Abstract

Improved intra-cellular security is addressed using device-specific RF fingerprints to mitigate malicious network activity that can occur through unauthorized use of digital identities. In air monitoring applications where physical equipment constraints are not overly restrictive, RF fingerprinting remains a viable option for providing regional intra-cellular security for systems such as cellular telephone and last mile WiMax networks. Proof-of-concept results are provided for GSM signals given they are readily available in most areas. Recent RF fingerprinting work has demonstrated average device classification accuracies (serial number identification) of 92% using OFDM-based 802.11a preamble responses at SNR = 6 dB. The goal here was to determine if similar performance could be achieved using RF fingerprints extracted from near-transient and midamble regions of GSM signals. This was done using instantaneous phase responses from each region to form RF statistical fingerprints that are subsequently classified using Fisher-based MDA/ML processing. Considering all GSM device permutations from four different manufacturers, near-transient RF fingerprinting provided nearly 13% improvement in classification performance when compared with midamble RF fingerprinting and achieved average classification performance consistent with the 802.11a benchmark of 92% correct classification at SNR = 6 dB.