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Overcoming Security Vulnerabilities in Deep Learning--based Indoor Localization Frameworks on Mobile Devices

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Published:15 November 2019Publication History
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Abstract

Indoor localization is an emerging application domain for the navigation and tracking of people and assets. Ubiquitously available Wi-Fi signals have enabled low-cost fingerprinting-based localization solutions. Further, the rapid growth in mobile hardware capability now allows high-accuracy deep learning--based frameworks to be executed locally on mobile devices in an energy-efficient manner. However, existing deep learning--based indoor localization solutions are vulnerable to access point (AP) attacks. This article presents an analysis into the vulnerability of a convolutional neural network--based indoor localization solution to AP security compromises. Based on this analysis, we propose a novel methodology to maintain indoor localization accuracy, even in the presence of AP attacks. The proposed secured neural network framework (S-CNNLOC) is validated across a benchmark suite of paths and is found to deliver up to 10× more resiliency to malicious AP attacks compared to its unsecured counterpart.

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  1. Overcoming Security Vulnerabilities in Deep Learning--based Indoor Localization Frameworks on Mobile Devices

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      Jawwad A Shamsi

      This paper analyzes "the vulnerability of a convolutional neural network (CNN) based indoor localization solution." The authors "propose a novel methodology to maintain indoor localization accuracy ... in the presence of access point (AP) attacks." Indoor localization is an emerging area and the paper is significant in this context. AP attacks can have variable impact on indoor localization accuracy. In extreme cases, such as emergency response, low accuracy in indoor localization can be fatal. The paper elaborates on the threat model and also describes background work. The experiment section of the paper is well written. The proposed technique, SCNNLOC, is compared with an existing CNN-based indoor localization framework (CNNLOC). On average, SCNNLOC is ten times more secure than CNNLOC. The authors consider various attacks "such as [wireless access point, WAP] spoofing, WAP jamming, and even environmental changes." An important area that still needs to be explored is time (or processing delay) in estimating the location. For critical applications such as emergency response systems, latency could be critical.

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