Network Traffic Analysis based IoT Device Identification

Published in 4th International Conference on Big Data and Internet of Things, BDIOT 2020, Singapore, 2020

Recommended citation: Chowdhury R., Aneja S., Aneja N., and Abas E. Network Traffic Analysis based IoT Device Identification. In Proceedings of the 2020 the 4th International Conference on Big Data and Internet of Things (BDIOT 2020). Association for Computing Machinery, New York, NY, USA, 79–89. DOI:https://doi.org/10.1145/3421537.3421545 https://dl.acm.org/doi/abs/10.1145/3421537.3421545

Abstract

Device identification is the process of identifying a device on Internet without using its assigned network or other credentials. The sharp rise of usage in Internet of Things (IoT) devices has imposed new challenges in device identification due to a wide variety of devices, protocols and control interfaces. In a network, conventional IoT devices identify each other by utilizing IP or MAC addresses, which are prone to spoofing. Moreover, IoT devices are low power devices with minimal embedded security solution. To mitigate the issue in IoT devices, fingerprint (DFP) for device identification can be used. DFP identifies a device by using implicit identifiers, such as network traffic (or packets), radio signal, which a device used for its communication over the network. These identifiers are closely related to the device hardware and software features. In this paper, we exploit TCP/IP packet header features to create a device fingerprint utilizing device originated network packets. We present a set of three metrics which separate some features from a packet which contribute actively for device identification. To evaluate our approach, we used publicly accessible two datasets. We observed the accuracy of device genre classification 99.37% and 83.35% of accuracy in the identification of an individual device from IoT Sentinel dataset. However, using UNSW dataset device type identification accuracy reached up to 97.78%.