Uniform TitleSecuring wireless localization against signal strength attacks
NameChen, Yingying (author), Martin, Richard (chair), Trappe, Wade (internal member), Madigan, David (internal member), Su, Wei (outside member), Rutgers University, Graduate School - New Brunswick,
Wireless communication systems,
Multisensor data fusion
DescriptionAccurately 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.
NoteIncludes bibliographical references (p. 125-129).
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
RightsThe author owns the copyright to this work.