By Topic

Using DE-Optimized LFS Processing to Enhance 4G Communication Security

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Harmer, P.K. ; Dept. of Electr. & Comput. Eng., Air Force Inst. of Technol., Wright-Patterson AFB, OH, USA ; Williams, M.D. ; Temple, Michael A.

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.

Published in:

Computer Communications and Networks (ICCCN), 2011 Proceedings of 20th International Conference on

Date of Conference:

July 31 2011-Aug. 4 2011