Overcoming Security Vulnerability of Indoor Localization on Mobile Devices
Available for Licensing
US Utility Patent Pending (Not Yet Published)
At A Glance
Researchers at Colorado State University have developed a novel secured methodology to maintain indoor localization accuracy, even in the presence of AP attacks. The proposed methodology consists of 1) Extrapolation and creation of the Secure AP Attack Resilient (SAAR) database; and 2) Training secure-CNN models (S-CNN) that are resilient to malicious AP based attacks.
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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 in-door localization solutions are vulnerable to access point (AP) attacks.
Malicious third parties can exploit the vulnerabilities of unsecured indoor localization components (e.g., Wi-Fi Access Points or WAPs) to produce incorrect localization information. This may lead to some inconvenience in non-critical scenarios (e.g., a student arrives at the wrong classroom), but can lead to dire consequences in more critical situations (e.g., medical staff are unable to locate vital equipment or medicine needed fora patient in an emergency; or emergency response personnel are misdirected, causing a loss of lives).
Hackers, however, may not be the only cause of vulnerabilities. Some vulnerabilities may arise from temporary and unintentional environmental changes that may alter the accuracy of indoor localization techniques. This tainted information from intentional or unintentional sources can lead to even more egregious real-time delays and errors. Therefore, similar to outdoor navigation systems, establishing secure and reliable indoor localization and navigation systems holds an uncontested importance in this domain
- High accuracy indoor localization
- Access Point attack resilient
- Approach was validated across a benchmark suite of paths and found to deliver up to 25x more resiliency
- Secure methodology
- Non-critical Indoor navigation (e.g., students to correct classrooms, consumers within stores)
- Critical indoor navigation (e.g., navigating medical staff and equipment closest to patients in real time; notification of emergency responders to serious health hazards, such as collapse or fire)
Tiku, Saideep, and Sudeep Pasricha. “Overcoming Security Vulnerabilities in Deep Learning–Based Indoor Localization Frameworks on Mobile Devices.” ACM Transactions on Embedded Computing Systems (TECS), 1 Nov. 2019, dl.acm.org/doi/10.1145/3362036.
Last updated: May 2020