TY - GEN
T1 - IoT Traffic Flow Identification using Locality Sensitive Hashes
AU - Charyyev, Batyr
AU - Gunes, Mehmet Hadi
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85089438601&partnerID=8YFLogxK
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U2 - 10.1109/ICC40277.2020.9148743
DO - 10.1109/ICC40277.2020.9148743
M3 - Conference contribution
AN - SCOPUS:85089438601
T3 - IEEE International Conference on Communications
BT - 2020 IEEE International Conference on Communications, ICC 2020 - Proceedings
T2 - 2020 IEEE International Conference on Communications, ICC 2020
Y2 - 7 June 2020 through 11 June 2020
ER -