TY - GEN
T1 - Protecting IoT-environments against Traffic Analysis Attacks with Traffic Morphing
AU - Hafeez, Ibbad
AU - Antikainen, Markku
AU - Tarkoma, Sasu
N1 - Funding Information:
This work was in part supported by Academy of Finland grant number 314008 and Doctoral Programme in Computer Sciences (DoCS) at University of Helsinki.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/3
Y1 - 2019/3
N2 - Traffic analysis attacks allow an attacker to infer sensitive information about users by analyzing network traffic of user devices. These attacks are passive in nature and are difficult to detect. In this paper, we demonstrate that an adversary, with access to upstream traffic from a smart home network, can identify the device types and user interactions with IoT devices, with significant confidence. These attacks are practical even when device traffic is encrypted because they only utilize statistical properties, such as traffic rates, for analysis. In order to mitigate the privacy implications of traffic analysis attacks, we propose a traffic morphing technique, which shapes network traffic thus making it more difficult to identify IoT devices and their activities. Our evaluation shows that the proposed technique provides protection against traffic analysis attacks and prevent privacy leakages for smart home users.
AB - Traffic analysis attacks allow an attacker to infer sensitive information about users by analyzing network traffic of user devices. These attacks are passive in nature and are difficult to detect. In this paper, we demonstrate that an adversary, with access to upstream traffic from a smart home network, can identify the device types and user interactions with IoT devices, with significant confidence. These attacks are practical even when device traffic is encrypted because they only utilize statistical properties, such as traffic rates, for analysis. In order to mitigate the privacy implications of traffic analysis attacks, we propose a traffic morphing technique, which shapes network traffic thus making it more difficult to identify IoT devices and their activities. Our evaluation shows that the proposed technique provides protection against traffic analysis attacks and prevent privacy leakages for smart home users.
UR - http://www.scopus.com/inward/record.url?scp=85067966956&partnerID=8YFLogxK
U2 - 10.1109/PERCOMW.2019.8730787
DO - 10.1109/PERCOMW.2019.8730787
M3 - Conference contribution
AN - SCOPUS:85067966956
T3 - 2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019
SP - 196
EP - 201
BT - 2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019
PB - IEEE
T2 - IEEE International Conference on Pervasive Computing and Communications Workshops
Y2 - 11 March 2019 through 15 March 2019
ER -