Adversarial Machine Learning for Wireless Localization

Wireless localization aims to use wireless technologies to obtain position-related information to locate the target. With the help of advanced machine learning techniques, position-related wireless data can be effectively extracted and analyzed to accurately predict the target locations. Despite the powerful deep learning models helping to improve the precision of localization, the black-box feature of models poses a crucial challenge to trustworthiness. In this chapter, various attack methods and defense schemes are evaluated in different wireless positioning systems. By examining the vulnerabilities in deep learning-driven localization systems, we demonstrate the necessity to construct a robust wireless localization system.