It is estimated that the number of IoT devices will reach 50 billion by 2030, with its wide range of applications and ease of use. However, in recent years, it has been established that there are numerous attacks targeting IoT devices and various challenges to securing them due to their limited computing capacity. One of the main problems that need to be solved is detecting and reporting malicious packets that are attempting to enter the IoT device before they are executed, also known as intrusion detection. An Intrusion Detection System (IDS) is a tool that monitors the network for potentially malicious packets and raises an alert when one is detected. With the usage of technologies such as machine learning and published datasets of IoT traffic that contain malicious traffic and normal traffic, an optimal approach to intrusion detection can be identified. This paper provides an overview of existing machine learning approaches for intrusion detection and evaluates different approaches using multiple datasets. The available datasets were evaluated on several machine learning models based on accuracy, F1-score, and efficiency.