TY - GEN
T1 - MAC-Layer Traffic Shaping Defense Against WiFi Device Fingerprinting Attacks
AU - Alyami, Mnassar
AU - Alkhowaiter, Mohammed
AU - Ghanim, Mansour Al
AU - Zou, Cliff
AU - Solihin, Yan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - WiFi networks are vulnerable to statistical traffic analysis attacks, even when a WiFi network is securely encrypted and the attacker is unable to join the network. Many defenses proposed in the literature are inefficient to deal with profiling attacks against WiFi-based Internet-of-Things (IoT) devices, because they burden the Internet traffic with high bandwidth overhead and pose deliberate delay on packet transmission. In this paper, we propose a new MAC-layer packet injection technique where injected dummy packets only exist within the WiFi link between IoT devices and their connected WiFi access point. This traffic shaping defense is effective against data-link device profiling attacks without adding any Internet-side overhead or time delay in legitimate traffic. We evaluated our approach on four WiFi-based IoT devices against a recent privacy attack, and showed the reduction of attack classification accuracy from the original 100% to 54%, close to random guessing..
AB - WiFi networks are vulnerable to statistical traffic analysis attacks, even when a WiFi network is securely encrypted and the attacker is unable to join the network. Many defenses proposed in the literature are inefficient to deal with profiling attacks against WiFi-based Internet-of-Things (IoT) devices, because they burden the Internet traffic with high bandwidth overhead and pose deliberate delay on packet transmission. In this paper, we propose a new MAC-layer packet injection technique where injected dummy packets only exist within the WiFi link between IoT devices and their connected WiFi access point. This traffic shaping defense is effective against data-link device profiling attacks without adding any Internet-side overhead or time delay in legitimate traffic. We evaluated our approach on four WiFi-based IoT devices against a recent privacy attack, and showed the reduction of attack classification accuracy from the original 100% to 54%, close to random guessing..
KW - Device Fingerprinting
KW - IoT Privacy
KW - Traffic Analysis Countermeasure
UR - http://www.scopus.com/inward/record.url?scp=85141137601&partnerID=8YFLogxK
U2 - 10.1109/ISCC55528.2022.9913056
DO - 10.1109/ISCC55528.2022.9913056
M3 - Conference contribution
AN - SCOPUS:85141137601
T3 - Proceedings - IEEE Symposium on Computers and Communications
BT - 2022 IEEE Symposium on Computers and Communications, ISCC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th IEEE Symposium on Computers and Communications, ISCC 2022
Y2 - 30 June 2022 through 3 July 2022
ER -