## 2 Related Work
### IoT Device Identification Methods
The experimental results demonstrated that the method could identify eleven called functions with an accuracy of 76.1% for the Amazon Echo Spot, nine functions with an accuracy of 89.8% for the Amazon Echo Dot, and had an accuracy of 85.2% for the Amazon Echo Flex. Moreover, a device identification method for the three types of Amazon Echo was implemented with an accuracy of 99.1%. This method can automatically and more precisely monitor how IoT devices operate than the previous method [7]. Nevertheless, the target is still limited to smart speakers only. [PERSON] et al. proposed a method to estimate the execution functions on several types of IoT devices [9], which is also an extended version of the previous two methods. To examine the possibility of analyzing which functions of an IoT device have been executed, they used eight IoT devices from four categories: two smart cameras, two smart remote controllers, two smart speakers, and two smart plugs. The devices were connected to an edge router, where traffic data were collected during the execution of each function 10 s before and after execution. From the collected traffic data, the number and size of sending, receiving, TCP, and UDP packets, as well as the number of source and destination IP addresses in time windows of 0.5, 1, and 1.5 s were extracted. The mean, maximum, variance, and standard deviation values of the 30 extracted items were computed and evaluated based on the importance of the features calculated using random forests. Thereafter, the method selected and used 28 important features from 120 features as feature values and identified the function of the IoT device executed using random forests. The accuracy of detecting eight functions was 73%, and the accuracy of detecting 16 combinations of eight executed functions and two IoT devices in each device category was 91%. This method enables the automatic understanding of the behavior of not only smart speakers but also a wide variety of IoT devices. [PERSON] et al. proposed an autonomous IoT device identification prototype [10], thereby outlining a methodology for an IoT device identification assistant and its architecture. The objective was to identify the types of devices that were newly connected to the home gateway to help end users better manage their devices and obtain more services from them. In feature selection, the first set of features was extracted from the flow characteristics of packet size, the interarrival time of flows, flow size, and protocol. In addition, the second set of features was extracted from the device description inspected from the packet payloads: the manufacturer name from the MAC address and the device name from the DHCP information. This method identified 28 IoT devices with an average accuracy of 98% using a decision tree. This method was considered for architectures designed to operate in smart home environments. However, all processing related to device identification was performed on a server, and the identification results were obtained on the web browser. This poses a problem in terms of user privacy protection. [PERSON] et al. proposed a method for visualizing IoT traffic characteristics and identifying IoT devices based on the average information content, which is known as information entropy [11]. This method analyzed IoT traffic properties by calculating the information entropy of the traffic parameters: the number of source and destination IP addresses, the number of source and destination ports, packet sizes, and the total amount of data for source IP addresses observed in five minutes. Subsequently, this method visualized them using behavior-shaped graphs. Moreover, an IoT device identification method based on the information entropy of IoT traffic features was implemented, and IoT devices were successfully identified with 94% accuracy. This method uses entropy to achieve time series feature extraction in IoT device communication. However, focusing on entropy in only a certain time interval could lead to inadequate feature extraction for some IoT devices.