Understanding network traffic characteristics of IoT devices plays a critical role in improving both the performance and security of IoT devices, including IoT device identification, classification, and anomaly detection. Although a number of existing research efforts have developed machine-learning based algorithms to help address the challenges in improving the security of IoT devices, none of them have provided detailed studies on the network traffic characteristics of IoT devices. In this paper we collect and analyze the network traffic generated in a typical smart homes environment consisting of a set of common IoT (and non-IoT) devices. We analyze the network traffic characteristics of IoT devices from three complementary aspects: remote network servers and port numbers that IoT devices connect to, flow-level traffic characteristics such as flow duration, and packet-level traffic characteristics such as packet inter-arrival time. Our study provides critical insights into the operational and behavioral characteristics of IoT devices, which can help develop more effective security and performance algorithms for IoT devices.

翻译：虽然一些现有的研究努力已经开发了基于机器学习的算法,以帮助应对在改善IoT装置安全方面的挑战,但没有一项研究对IoT装置的网络交通特点进行了详细研究。在这份文件中,我们收集和分析了由一套通用IoT(和非IoT)装置组成的典型智能家庭环境中产生的网络交通特点。我们从三个互补方面分析了IoT装置的网络交通特点:与IoT装置连接的远程网络服务器和港口号码、流动时间等流动水平交通特点以及跨入境时间等包包级交通特点。我们的研究提供了对IoT装置操作和行为特点的重要见解,有助于为IoT装置制定更有效的安全和性能算法。