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

• Published in: ACM transactions on embedded computing systems Vol. 18; no. 6; pp. 1 - 24
• Main Authors: Tiku, Saideep, Pasricha, Sudeep
• Format: Journal Article
• Language: English
• Published: 01.01.2020
• ISSN: 1539-9087; 1558-3465
• DOI: 10.1145/3362036

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.