The proliferation and diversity of Internet of things (IoT) devices increase IoT system risks. This open many challenging area for research like identifying authorized and unauthorized IoT devices, classifying events of devices, detecting network traffic anomalies generated by such devices, etc. To do so, machine and deep learning algorithms should be applied after extracted a set of features from the associated device’s traffic. The identification accuracy as well as the computational time are both very important factors, especially with the limited resources system. These considerations are firstly dependent on which features list is taken. So, the IoT network traffic analysis is the more important and difficult step in IoT device identification. In this paper, the traffic of six IoT home devices is analyzed using wireshark network protocol analyzer. A set of features and protocols are studied to gain insight into the important ones that can be utilized for creating a device fingerprint for identification purposes in the IoT environment. The result of the analytical study was that some features are unsuitable to be certified in the case of the testbed devices' traffic, while others are expressive features that can be used to identify devices according to their manufacturers.