## 2 Related Work
### Considerations Regarding IoT Device Identification in Smart Home Environments  
As mentioned previously, several IoT device identification methods based on traffic analysis with machine learning have been proposed. They automatically identify and understand IoT devices using traffic data. However, no consideration has been given to the implementation of these methods in a smart home environment.  
IoT device identification in smart home environments involves three considerations. The first is the need to update identification models. This is because it is unlikely that IoT devices, once installed, will be used indefinitely in smart home environments. As new devices are installed and older devices are removed, frequent updating of the identification model and periodic training for model updating are essential to achieve proper management in response to the replacement of IoT devices installed in smart homes. However, conventional methods implement IoT device identification with only one-time learning and do not consider the computational and communication loads associated with model updating.  
The second problem is how to process the traffic data. Analyzing a large amount of traffic data simultaneously in one place can cause a heavy load on the memory and CPU, as well as interfere with the analysis. With the growing use of IoT devices, the processing of a larger amount of collected IoT traffic is required. Although collecting and analyzing traffic data on a cloud server is one solution, this approach requires sending traffic data captured in smart homes to the cloud server every time the identification model is trained, which is unsuitable from the perspective of communication cost. Additionally, since IoT traffic contains a large amount of user information, uploading all the traffic data to the cloud server could lead to the leakage of user privacy. In addition, since the frequency of device usage and the amount of communication data vary by IoT device, biases potentially exist in the IoT traffic. The biases increase in proportion to the amount of traffic data, which potentially has a negative influence on traffic analysis. Therefore, it is necessary to consider how to process significant amounts of traffic data appropriately when implementing this method in smart home environments. However, conventional methods typically process traffic data simultaneously.  
The third consideration is the target range for IoT device identification. Specific IoT devices in smart homes can be identified simply using static information such as IP and MAC addresses obtained from traffic data. From the perspective of security in smart homes, however, it is important to not only determine whether the appropriate IoT devices are connected but also to identify and grasp whether they are operating properly. Conventional methods analyze IoT traffic based on the communication properties, such as the number of destination IP addresses, protocol types, and the total amount of data. However, they only focus on the features extracted from traffic data for a fixed interval, and they accordingly ignore that each IoT device has different communication and operation cycles. Thus, they overlook the behavior of IoT devices because they cannot extract features from some IoT traffic owing to variations in the communication periods or behavior.