Enabling Security for Fingerprinting-Based Indoor Localization on Mobile Devices

Abstract

Indoor localization, positioning, and navigation are an upcoming application domain for the navigation and tracking of people and assets. Ubiquitously available WiFi 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 frameworks are vulnerable to access point (AP) attacks. This chapter presents an analysis into the vulnerability of a convolutional neural network (CNN)-based indoor localization solution to AP security compromises. On the basis of this research, we offer a unique strategy for maintaining indoor localization accuracy despite AP attacks. The proposed secured framework (named S-CNNLOC) is validated over a benchmark suite of paths and is proven to be up to ten times more resistant to malicious AP attacks than its unprotected version.