Wireless communication networks remain under attack with ill- intentioned "hackers" routinely gaining unauthorized access through Wireless Access Points-one of the most vulnerable points in an Information Technology (IT) system. The goal here is to demonstrate the feasibility of using Radio Frequency (RF) air monitoring to augment conventional bit-level security at WAPs. The specific networks of interest include those based on Orthogonal Frequency Division Multiplexing (OFDM), to include 802.11a/g WiFi and 4G 802.16 WiMAX. Proof-of-concept results are presented to demonstrate the effectiveness of a "Learning from Signals" (LFS) classifier with Gaussian kernel bandwidth parameters optimally determined using Differential Evolution (DE). The resultant DE- optimized LFS classifier is implemented within an RF "Distinct Native Attribute" (RF-DNA) fingerprinting process with both Time Domain (TD) and Spectral Domain (SD) features input to the classifier. The RF-DNA is used for intra-manufacturer (like-model devices from a given manufacturer) discrimination of IEEE compliant 802.11a WiFi devices and 802.16e WiMAX devices. A comparative performance assessment is provided using results from the proposed DE-optimized LFS classifier and a Bayesian-based Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) classifier as used in previous demonstrations. The assessment is performed using identical TD and SD fingerprint features for both classifiers. Preliminary results of the DE-optimized classifier are very promising, with correct classification improvement of 15% to 40% realized over the range of signal to noise ratios considered.
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