Date of Award

5-2023

Document Type

Thesis

Degree Name

Master of Science (MS)

College/School

College of Science and Mathematics

Department/Program

Computer Science

Thesis Sponsor/Dissertation Chair/Project Chair

Jiacheng Shang

Committee Member

Bharath Samanthula

Committee Member

John Jenq

Abstract

Today's wireless networks (Wi-Fi) handle more significant numbers of connections, deploy efficiently, and provide increased reliability and high speeds at low cost. The ability of rogue access points (RAPs) to mimic legitimate APs makes them the most critical threat to wireless security. APs are found in coffee shops, supermarkets, stadiums, buses, trains, airports, hospitals, theaters, and shopping malls.

Rogue access points (RAP) are unauthorized devices that connect to legitimate access points and networks and bypass authorized security procedures. RAP detection has been attempted using hardware and software-based solutions requiring the developing of dedicated tools or beacon frame modification. (Arisandi, 2021). The effectiveness of software-based tools such as Aircrack-ng, Kismet, and InSSIDER is diminished as customized configurations are required for each environment. (VanSickle, 2019).

Channel State Information (CSI) are characteristics of the communication link between a Wi-Fi transmitter and receiver and facilitates reliable communication in multi-antenna systems. The data contained in CSI can be analyzed and used to detect motion and activity based on interference in the line of sight (LoS) between the transmitter and receiver. CSI has been used to recognize human activity (Wang, 2015) and recognize differences in gaits based on the speed of motion (Wang, 2016).

This paper proposes identifying RAPs by detecting differences in CSI characteristics due to interference in the (LoS) path between the Wi-Fi transmitter and the receiver.

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