With the development of IoT technology, IoT devices are widely applied in many areas of production and life. However, IoT devices also bring severe challenges to equipment asset management and security management. Firstly, Due to the diversity of device types and access modes in IoT devices, it is often difficult for administrators to know the network's device types and operating status. Secondly, IoT devices are becoming the focus of cyber attacks due to their limited computing and storage resources, making it difficult to deploy traditional defense measures. Therefore, it is particularly important to acknowledge the IoT devices in the network through device identification and detect anomalies based on the device identification results to ensure their normal operation. In recent years, the academia has carried out a lot of research on the above issues. This paper systematically reviews the work related to IoT devices' identification and anomaly detection. In terms of device identification, existing researches can be divided into passive identification methods and active identification methods according to whether packets are sent to the network. The passive identification methods are further investigated according to the identification method, identification granularity and application scenarios. We also investigate the active methods according to the identification method, identification granularity and detection granularity. In the aspect of anomaly detection, the existing work can be divided into detection methods based on machine learning and detection methods based on normal behavior. On this basis, IoT device identification and anomaly detection challenges are summarized, and its future development direction is proposed.