  Empowered by deep neural networks (DNNs), Wi-Fi fingerprinting has recently
achieved astonishing localization performance to facilitate many
security-critical applications in wireless networks, but it is inevitably
exposed to adversarial attacks, where subtle perturbations can mislead DNNs to
wrong predictions. Such vulnerability provides new security breaches to
malicious devices for hampering wireless network security, such as
malfunctioning geofencing or asset management. The prior adversarial attack on
localization DNNs uses additive perturbations on channel state information
(CSI) measurements, which is impractical in Wi-Fi transmissions. To transcend
this limitation, this paper presents FooLoc, which fools Wi-Fi CSI
fingerprinting DNNs over the realistic wireless channel between the attacker
and the victim access point (AP). We observe that though uplink CSIs are
unknown to the attacker, the accessible downlink CSIs could be their reasonable
substitutes at the same spot. We thoroughly investigate the multiplicative and
repetitive properties of over-the-air perturbations and devise an efficient
optimization problem to generate imperceptible yet robust adversarial
perturbations. We implement FooLoc using commercial Wi-Fi APs and Wireless
Open-Access Research Platform (WARP) v3 boards in offline and online
experiments, respectively. The experimental results show that FooLoc achieves
overall attack success rates of about 70% in targeted attacks and of above 90%
in untargeted attacks with small perturbation-to-signal ratios of about -18dB.

Cryptography and Security