Detection of Man-in-the-middle Attacks Using Physical Layer Wireless Security Techniques


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In a wireless network environment, all the users are able to access the wireless channel. Thus, if malicious users exploit this feature by mimicking the characteristics of a normal user or even the central wireless access point (AP), they can intercept almost all the information through the network. This scenario is referred as a Man-in-the-middle (MITM) attack. In the MITM attack, the attackers usually set up a rogue AP to spoof the clients. In this thesis, we focus on the detection of MITM attacks in Wi-Fi networks. The thesis introduces the entire process of performing and detecting the MITM attack in two separate sections. The first section starts from creating a rogue AP by imitating the characteristics of the legitimate AP. Then a multi-point jamming attack is conducted to kidnap the clients and force them to connect to the rogue AP. Furthermore, the sniffer software is used to intercept the private information passing through the rogue AP. The second section focuses on the detection of MITM attacks from two aspects: jamming attacks detection and rogue AP detection. In order to enable the network to perform defensive strategies more effectively, distinguishing different types of jamming attacks is necessary. We begin by using signal strength consistency mechanism in order to detect jamming attacks. Then, based on the statistical data of packets send ratio (PSR) and packets delivery ratio (PDR) in different jamming situations, a model is built to further differentiate the jamming attacks. At the same time, we gather the received signal strength indication (RSSI) values from three monitor nodes which process the random RSSI values employing a sliding window algorithm. According to the mean and standard deviation curve of RSSI, we can detect if a rogue AP is present within the vicinity. All these proposed approaches, either attack or detection, have been validated via computer simulations and experimental hardware implementations including Backtrack 5 Tools and MATLAB software suite.

Last modified
  • 2021-02-03
  • English
  • etd-082713-125108
Defense date
  • 2013
Date created
  • 2013-08-27
Resource type
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