Adversarial robustness of machine learning-based indoor positioning systems

Peter Swartz; Kevin Hobbs; Levi Hancock; Raed Salih; Michael R. Clark

doi:10.1117/12.2585596

12 April 2021 Adversarial robustness of machine learning-based indoor positioning systems

Peter Swartz, Kevin Hobbs, Levi Hancock, Raed Salih, Michael R. Clark

Proceedings Volume 11751, Disruptive Technologies in Information Sciences V; 117510G (2021) https://doi.org/10.1117/12.2585596
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Determining position within an indoor environment can be difficult when GPS signals become too weak. For this reason, alternatives are desired for indoor positioning systems (IPSes). The Bluetooth Low Energy (BLE) protocol is one alternative solution for IPSes. BLE is a low power wireless technology used for connecting devices with each other. There are two different methods for using BLE for localization: deterministic, and machine learning (ML) models. Each method uses a measured received signal strength indicator (RSSI) to determine distances from fixed, known locations. Deterministic models rely on empirical equations relating signal strength to distance, while ML uses collected signal strengths, or fingerprints, to learn positions. This paper assesses the robustness of an IPS system we built that uses BLE and ML by executing a distance fraud attack. A distance fraud attack causes intentional miscalculations of positions. The attack executed on the system assumes the attacker has network access and has compromised some small fraction of the receiving nodes. The results show a significant difference between the calculated positions of the system operating under benign conditions and operating under attack. We explore one possible defense against this attack by training an ML system for attack identification.

Conference Presentation

Citation Download Citation

Peter Swartz, Kevin Hobbs, Levi Hancock, Raed Salih, and Michael R. Clark "Adversarial robustness of machine learning-based indoor positioning systems", Proc. SPIE 11751, Disruptive Technologies in Information Sciences V, 117510G (12 April 2021); https://doi.org/10.1117/12.2585596

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