An Internet of Things (IoT) botnet is a network of Internet-connected devices, such as smart thermostats, security cameras, and home appliances that have been compromised by cybercriminals and are being used to carry out malicious activities, such as distributed denial of service (DDoS) attacks. These devices are often compromised through weak passwords, unpatched software vulnerabilities, or other security weaknesses. When anomaly detection models are trained on datasets that are likely unbalanced, the outcomes are insufficient. In contrast, the ability of Generative Adversarial Networks (GANs) to simulate the complicated high-dimensional distributions observed in real-world data suggests that they may be useful for anomaly identification. In this research we provide a framework that uses GANs to help in identify attacks on Internet of Things networks. The effectiveness of the approach is assessed using SDmatric.