TY - GEN
T1 - Entropy-based IoT devices identification
AU - Nguyen-An, Hung
AU - Silverston, Thomas
AU - Yamazaki, Taku
AU - Miyoshi, Takumi
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by JSPS KAKENHI Grant Number JP18K11287.
Publisher Copyright:
© 2020 KICS.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/9
Y1 - 2020/9
N2 - The Internet of Things is now part of everyday life and there has been a wide range of novel IoT applications collecting cyber-physical data and providing information on the environment. As it is expected that the IoT traffic will count for a major part of the Internet traffic, it is essential to characterize the IoT traffic and to identify each device, and especially in the case of cyberattacks. In this paper, we present a new method to identify IoT devices based on traffic entropy. We compute the entropy values of traffic features and we rely on Machine Learning algorithms to classify the traffic. Our method succeeds in identifying devices under various network conditions with performances up to 94% in all cases. Our method is also robust to unpredictable network behavior with anomalies spreading into the network.
AB - The Internet of Things is now part of everyday life and there has been a wide range of novel IoT applications collecting cyber-physical data and providing information on the environment. As it is expected that the IoT traffic will count for a major part of the Internet traffic, it is essential to characterize the IoT traffic and to identify each device, and especially in the case of cyberattacks. In this paper, we present a new method to identify IoT devices based on traffic entropy. We compute the entropy values of traffic features and we rely on Machine Learning algorithms to classify the traffic. Our method succeeds in identifying devices under various network conditions with performances up to 94% in all cases. Our method is also robust to unpredictable network behavior with anomalies spreading into the network.
KW - Anomaly
KW - Entropy
KW - Identification
KW - IoT
KW - Traffic Analysis
UR - http://www.scopus.com/inward/record.url?scp=85096971207&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85096971207&partnerID=8YFLogxK
U2 - 10.23919/APNOMS50412.2020.9236963
DO - 10.23919/APNOMS50412.2020.9236963
M3 - Conference contribution
AN - SCOPUS:85096971207
T3 - APNOMS 2020 - 2020 21st Asia-Pacific Network Operations and Management Symposium: Towards Service and Networking Intelligence for Humanity
SP - 73
EP - 78
BT - APNOMS 2020 - 2020 21st Asia-Pacific Network Operations and Management Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st Asia-Pacific Network Operations and Management Symposium, APNOMS 2020
Y2 - 22 September 2020 through 25 September 2020
ER -