  The Internet of Things (IoT) and massive IoT systems are key to
sixth-generation (6G) networks due to dense connectivity, ultra-reliability,
low latency, and high throughput. Artificial intelligence, including deep
learning and machine learning, offers solutions for optimizing and deploying
cutting-edge technologies for future radio communications. However, these
techniques are vulnerable to adversarial attacks, leading to degraded
performance and erroneous predictions, outcomes unacceptable for ubiquitous
networks. This survey extensively addresses adversarial attacks and defense
methods in 6G network-assisted IoT systems. The theoretical background and
up-to-date research on adversarial attacks and defenses are discussed.
Furthermore, we provide Monte Carlo simulations to validate the effectiveness
of adversarial attacks compared to jamming attacks. Additionally, we examine
the vulnerability of 6G IoT systems by demonstrating attack strategies
applicable to key technologies, including reconfigurable intelligent surfaces,
massive multiple-input multiple-output (MIMO)/cell-free massive MIMO,
satellites, the metaverse, and semantic communications. Finally, we outline the
challenges and future developments associated with adversarial attacks and
defenses in 6G IoT systems.
