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Securing wireless localization against signal strength attacks
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Title
Securing wireless localization against signal strength attacks
Identifier
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.15800
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ETD_266
Language
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eng
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theses
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Topic
Computer Science
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Topic
Wireless communication systems
Subject
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(authority = ETD-LCSH)
Topic
Sensor networks
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(authority = ETD-LCSH)
Topic
Detectors
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Multisensor data fusion
Abstract
Accurately positioning nodes in wireless and sensor networks is important because the location of devices and sensors is a critical input to many higher-level applications. However, the localization infrastructure can be subjected to non-cryptographic attacks, such as signal attenuation and amplification, that can not be addressed by traditional security services. This thesis aims to provide secure and accurate location information in wireless and sensor networks by characterizing the response of localization algorithms to attacks, detecting attacks, localizing adversaries, and additionally, improving localization performance.
First we studied the robustness of localization algorithms to signal strength attacks. We found the performance of localization algorithms degrades significantly under attacks when signals are attenuated or amplified by an adversary. We then formulated a theoretical foundation for the attack detection problem using statistical significance testing. We proposed attack detection schemes for two broad localization approaches: signal strength and multilateration. We found that different localization systems all contain similar attack detection capabilities. Next, we examined the applicability of localization methods to localize adversaries participating in identity-based spoofing attacks. We proposed a spoofing detector for wireless spoofing that utilizes K-means cluster analysis. We integrated our K-means attack detector into a real-time indoor localization system, which is capable of localizing the positions of attackers. Our experiments using both an 802.11 (WiFi) network as well as an 802.15.4 (ZigBee) network in two office buildings provide strong evidence of the effectiveness of our approach in attack detection and localizing the positions of the adversaries.
In addition, we investigated the impact of landmark placement on localization performance using a combination of analytic and experimental analysis. We developed a novel algorithm called maxL-minE algorithm that finds an optimized landmark deployment. Our experimental results show that our landmark placement algorithm is generic because the resulting placements improve localization performance significantly across a diverse set of algorithms, networks, and ranging modalities. Finally, we presented our general purpose real time localization infrastructure which targets to localize any radio-enabled wireless devices at anywhere and at anytime.
First we studied the robustness of localization algorithms to signal strength attacks. We found the performance of localization algorithms degrades significantly under attacks when signals are attenuated or amplified by an adversary. We then formulated a theoretical foundation for the attack detection problem using statistical significance testing. We proposed attack detection schemes for two broad localization approaches: signal strength and multilateration. We found that different localization systems all contain similar attack detection capabilities. Next, we examined the applicability of localization methods to localize adversaries participating in identity-based spoofing attacks. We proposed a spoofing detector for wireless spoofing that utilizes K-means cluster analysis. We integrated our K-means attack detector into a real-time indoor localization system, which is capable of localizing the positions of attackers. Our experiments using both an 802.11 (WiFi) network as well as an 802.15.4 (ZigBee) network in two office buildings provide strong evidence of the effectiveness of our approach in attack detection and localizing the positions of the adversaries.
In addition, we investigated the impact of landmark placement on localization performance using a combination of analytic and experimental analysis. We developed a novel algorithm called maxL-minE algorithm that finds an optimized landmark deployment. Our experimental results show that our landmark placement algorithm is generic because the resulting placements improve localization performance significantly across a diverse set of algorithms, networks, and ranging modalities. Finally, we presented our general purpose real time localization infrastructure which targets to localize any radio-enabled wireless devices at anywhere and at anytime.
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xiv, 131 pages
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Ph.D.
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Includes bibliographical references (p. 125-129).
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Chen
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Yingying
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Yingying Chen
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Martin
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Richard
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Richard P. Martin
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Wade
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David
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David Madigan
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Su
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Wei
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Wei Su
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Rutgers University
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Graduate School - New Brunswick
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2007
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2007
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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doi:10.7282/T33T9HPW
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ETD doctoral
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Open
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Yingying Chen
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Rutgers University. Graduate School - New Brunswick
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Non-exclusive ETD license
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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