The field of cybersecurity in IoT networks is moving towards the development of more robust and efficient anomaly detection methods. Recent work has focused on improving the accuracy and reliability of intrusion detection systems, particularly in the face of increasingly sophisticated attacks. One notable trend is the incorporation of machine learning and deep learning techniques, such as generative adversarial networks and trust-based models, to enhance detection capabilities. These approaches have shown significant promise in identifying and mitigating threats in dynamic and diverse IoT environments. Noteworthy papers include: SD-CGAN, which proposes a conditional generative adversarial network framework for robust anomaly detection in IoT edge environments, and A Novel Trust-Based DDoS Cyberattack Detection Model, which introduces a trust-based approach for detecting DDoS attacks in smart business environments.