Abstract
Access control is a common part of network security measure. However, the existing access control mechanism is mostly limited to distinguish users based on digital authentication methods such as MAC addresses, which are vulnerable to forgery and counterfeiting attacks. Using distinguishable physical-layer (PHY) based on fingerprints from devices and MAC-based ID verification is a popular method for enhancing network security. Extracting device finger-prints is the crucial step in this enhanced security method. In this paper, different from the existing fingerprint extraction method based on the preamble, we propose an adaptive filter-based method of fingerprint extraction. The method utilizes more signal regions to extract finer fingerprints, which is not limited to the preamble. Our results show that with a filter order of 65 and 20 times averaging, among 13 devices can be classified with 98% accuracy under the Multiple Discriminant Analysis, Maximum Likelihood (MDA/ML) classifier.
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