  While the benefits of 6G-enabled Internet of Things (IoT) are numerous,
providing high-speed, low-latency communication that brings new opportunities
for innovation and forms the foundation for continued growth in the IoT
industry, it is also important to consider the security challenges and risks
associated with the technology. In this paper, we propose a two-stage intrusion
detection framework for securing IoTs, which is based on two detectors. In the
first stage, we propose an adversarial training approach using generative
adversarial networks (GAN) to help the first detector train on robust features
by supplying it with adversarial examples as validation sets. Consequently, the
classifier would perform very well against adversarial attacks. Then, we
propose a deep learning (DL) model for the second detector to identify
intrusions. We evaluated the proposed approach's efficiency in terms of
detection accuracy and robustness against adversarial attacks. Experiment
results with a new cyber security dataset demonstrate the effectiveness of the
proposed methodology in detecting both intrusions and persistent adversarial
examples with a weighted avg of 96%, 95%, 95%, and 95% for precision, recall,
f1-score, and accuracy, respectively.
