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.

翻译：尽管6G赋能的物联网（IoT）带来众多优势——实现高速低延迟通信，为创新开辟新机遇，并为物联网行业的持续增长奠定基础——但我们也必须重视相关技术带来的安全挑战与风险。本文提出一种基于双重检测器的两阶段入侵检测框架，用于保障物联网安全。在第一阶段，我们提出一种采用生成对抗网络（GAN）的对抗训练方法，通过向第一个检测器提供对抗样本作为验证集，帮助其在鲁棒特征上进行训练。由此，分类器将能有效抵御对抗攻击。随后，我们为第二阶段检测器设计了一个深度学习（DL）模型用于识别入侵行为。我们评估了所提方法在检测准确率及对抗攻击鲁棒性方面的效能。实验基于新的网络安全数据集进行，结果表明该方法在检测入侵与持续性对抗样本方面具有有效性，其加权平均精确率、召回率、F1分数和准确率分别达到96%、95%、95%和95%。