Abstract Systems get smarter with computing capabilities, especially in the form of Internet of Things (IoT) devices. IoT devices are often resource-limited as they are optimized for a certain task. Hence, they are prone to be compromised and have become a target of malicious activities. Since IoT devices lack computing power for security software, network administrators need to isolate such devices and limit traffic to the device based on their communication needs. To this end, network administrators need to identify IoT devices when they join a network and detect anomalous traffic when they are compromised. In this paper, we introduce a novel approach to identify the IoT device based on the Nilsimsa hash of its traffic flow. Different from previous studies, the proposed approach does not require feature extraction from the data. In our evaluations, our approach has an average precision and recall of 93% and 90%, respectively.
Dataset description
Dataset contains network traffic of 22 IoT devices collected for 20 days.
Please Cite as:
B. Charyyev and M. H. Gunes, "Locality-Sensitive IoT Network Traffic Fingerprinting for Device Identification," in IEEE Internet of Things Journal, vol. 8, no. 3, pp. 1272-1281, 1 Feb.1, 2021, doi: 10.1109/JIOT.2020.3035087.
B. Charyyev and M. H. Gunes, "IoT Traffic Flow Identification using Locality Sensitive Hashes," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9148743.