Original scientific paper
https://doi.org/10.2498/cit.1002530
Detecting and Locating Man-in-the-Middle Attacks in Fixed Wireless Networks
Ziqian (Cecilia) Dong
; School of Engineering and Computing Sciences, New York Institute of Technology, New York, USA
Randolph Espejo
; School of Engineering and Computing Sciences, New York Institute of Technology, New York, USA
Yu Wan
; School of Engineering and Computing Sciences, New York Institute of Technology, New York, USA
Wenjie Zhuang
; School of Engineering and Computing Sciences, New York Institute of Technology, New York, USA
Abstract
We propose a novel method to detect and locate a Man-in-the-Middle attack in a fixed wireless network by analyzing round-trip time and measured received signal strength from fixed access points. The proposed method was implemented as a client-side application that establishes a baseline for measured round trip time (RTTs) and received signal strength (RSS) under no-threat scenarios and applies statistical measures on the measured RTT and RSS to detect and locate Man-in-the-Middle attacks.
We show empirically that the presence of a Man-in-the-Middle attack incurs a significantly longer delay and larger standard deviation in measured RTT compared to that measured without a Man-in-the-Middle attack.
We evaluated three machine learning algorithms on the measured RSS dataset to estimate the location of a Man-in-the-Middle attacker.
Experimental results show that the proposed method can effectively detect and locate a Man-in-the-Middle attack and achieves a mean location estimation error of 0.8 meters in an indoor densely populated metropolitan
environment.
Keywords
Man-in-the-Middle; Wi-Fi; fixed wireless network; location estimation; timing analysis; machine learning
Hrčak ID:
149732
URI
Publication date:
18.12.2015.
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