TY - GEN
T1 - Detecting consumer IoT devices through the lens of an ISP
AU - Saidi, Said Jawad
AU - Mandalari, Anna Maria
AU - Haddadi, Hamed
AU - Dubois, Daniel J.
AU - Choffnes, David
AU - Smaragdakis, Georgios
AU - Feldmann, Anja
PY - 2021
Y1 - 2021
N2 - Internet of Things (IoT) devices are becoming increasingly popular and offer a wide range of services and functionality to their users. However, there are significant privacy and security risks associated with these devices. IoT devices can infringe users' privacy by ex-filtrating their private information to third parties, often without their knowledge. In this work we investigate the possibility to identify IoT devices and their location in an Internet Service Provider's network. By analyzing data from a large Internet Service Provider (ISP), we show that it is possible to recognize specific IoT devices, their vendors, and sometimes even their specific model, and to infer their location in the network. This is possible even with sparsely sampled flow data that are often the only datasets readily available at an ISP. We evaluate our proposed methodology [1] to infer IoT devices at subscriber lines of a large ISP. Given ground truth information on IoT devices location and models, we were able to detect more than 77% of the studied IoT devices from sampled flow data in the wild.
AB - Internet of Things (IoT) devices are becoming increasingly popular and offer a wide range of services and functionality to their users. However, there are significant privacy and security risks associated with these devices. IoT devices can infringe users' privacy by ex-filtrating their private information to third parties, often without their knowledge. In this work we investigate the possibility to identify IoT devices and their location in an Internet Service Provider's network. By analyzing data from a large Internet Service Provider (ISP), we show that it is possible to recognize specific IoT devices, their vendors, and sometimes even their specific model, and to infer their location in the network. This is possible even with sparsely sampled flow data that are often the only datasets readily available at an ISP. We evaluate our proposed methodology [1] to infer IoT devices at subscriber lines of a large ISP. Given ground truth information on IoT devices location and models, we were able to detect more than 77% of the studied IoT devices from sampled flow data in the wild.
KW - internet measurement
KW - internet of things
KW - IoT detection
KW - IoT security and privacy
UR - http://www.scopus.com/inward/record.url?scp=85112196906&partnerID=8YFLogxK
U2 - 10.1145/3472305.3472885
DO - 10.1145/3472305.3472885
M3 - Conference contribution
AN - SCOPUS:85112196906
T3 - ANRW 2021 - Proceedings of the 2021 Applied Networking Research Workshop
SP - 36
EP - 38
BT - ANRW 2021 - Proceedings of the 2021 Applied Networking Research Workshop
PB - Association for Computing Machinery (ACM)
T2 - 2021 IRTF Applied Networking Research Workshop, ANRW 2021
Y2 - 24 July 2021 through 30 July 2021
ER -