The development of advanced intrusion detection systems (IDS) has significantly enhanced the use of Machine Learning (ML) techniques for addressing security challenges in Internet of Things (IoT) environments. These systems apply ML algorithms to monitor network activity and identify malicious behavior that may indicate potential security breaches. ML-based IDS are capable of detecting where adversaries exploit previously known vulnerabilities within IoT networks. Accuracy, Precision, and the F1 score are common criteria used to evaluate these systems’ effectiveness. The F1 score offers a balanced evaluation of the IDS performance by combining accuracy and precision, which show how well the system predicts overall. Precision evaluates the fraction of actual positive detections among all positive predictions, and accuracy indicates how well the system predicts in general. By using these ML technologies, IoT networks can strengthen their security and better withstand new attacks.