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A Position Self-Adaptive Method to Detect Fake Access Points

Ping Lu1,2,*

1 Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
2 Guizhou University, Sate Key Laboratory of Public Big Data, Guiyang, 550025, China

* Corresponding Author: Ping Lu. Email: email

Journal of Quantum Computing 2020, 2(2), 119-127. https://doi.org/10.32604/jqc.2020.09433

Abstract

In recent years, with the maturity and popularity of Wi-Fi technology, wireless hotspots have been deployed on a large scale in public places. But at the same time, it brings many security issues that cannot be ignored. Among them, the fake access point attack is a very serious threat in wireless local area network. In this paper, we propose a method to detect fake access points in wireless local area network. First, our detection method is passive, which means there is almost no additional traffic will be generated during the program’s operation. Second, different from many existing methods, our method allows the detection device to change position, the move will be perceived and the fingerprint will be updated automatically. Third, we use a variety of features as fingerprints to describe an access point better and improve efficiency. At last, the method we propose is more in line with the actual scene and has been proved effective by experiments.

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Cite This Article

APA Style
Lu, P. (2020). A position self-adaptive method to detect fake access points. Journal of Quantum Computing, 2(2), 119-127. https://doi.org/10.32604/jqc.2020.09433
Vancouver Style
Lu P. A position self-adaptive method to detect fake access points. J Quantum Comput . 2020;2(2):119-127 https://doi.org/10.32604/jqc.2020.09433
IEEE Style
P. Lu, “A Position Self-Adaptive Method to Detect Fake Access Points,” J. Quantum Comput. , vol. 2, no. 2, pp. 119-127, 2020. https://doi.org/10.32604/jqc.2020.09433

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cc Copyright © 2020 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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