Using differential evolution to optimize 'learning from signals' and enhance network security

Authors

Paul K. Harmer, Air Force Institute of Technology
Michael A. Temple, Air Force Institute of TechnologyFollow
Mark A. Buckner, Oak Ridge National Laboratory
Ethan Farquahar, Oak Ridge National Laboratory

Document Type

Conference Proceeding

Publication Date

7-12-2011

Abstract

Excerpt: Computer and communication network attacks are commonly orchestrated through Wireless Access Points (WAPs). This paper summarizes proof-of-concept research activity aimed at developing a physical layer Radio Frequency (RF) air monitoring capability to limit unauthorized WAP access and improve network security. This is done using Differential Evolution (DE) to optimize the performance of a "Learning from Signals" (LFS) classifier implemented with RF "Distinct Native Attribute" (RF-DNA) fingerprints.

Comments

Copyright © 2011 ACM.

DOI

10.1145/2001576.2001819

Source Publication

Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11

Recommended Citation

Paul K. Harmer, Michael A. Temple, Mark A. Buckner, and Ethan Farquahar. 2011. Using differential evolution to optimize 'learning from signals' and enhance network security. In Proceedings of the 13th annual conference on Genetic and evolutionary computation (GECCO '11). Association for Computing Machinery, New York, NY, USA, 1811–1818. https://doi.org/10.1145/2001576.2001819