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

Generative Adversarial Networks-Driven Cyber Threat Intelligence Detection Framework for Securing Internet of Things