As a quintessential example of cyber-physical systems in transportation, maglev transport systems have profoundly transformed modern transportation through their efficient, precise, and autonomous operation capabilities. However, the system’s high level of informatization and intelligence also poses severe cybersecurity challenges. Attack methods such as sensor spoofing, erroneous data injection, and denial-of-service seriously threaten the system's state perception, decision-making, and control security. This paper systematically analyzes three critical components in maglev transport systems: attack detection, state assessment, and security control. Regarding attack detection, it compares model-based and data-driven approaches. For state assessment, it explores security estimation strategies based on optimization and robust statistics. Concerning security control, it elaborates on the design principles of passive and active resilience control. The effectiveness and robustness of the proposed methods under false data injection attacks were validated through a small unmanned vehicle simulation platform. This paper aims to provide theoretical support and technical pathways for constructing highly reliable and secure maglev systems.