  Nowadays, attackers target Internet of Things (IoT) devices for security
exploitation, and search engines for devices and services compromise user
privacy, including IP addresses, open ports, device types, vendors, and
products.Typically, application banners are used to recognize IoT device
profiles during network measurement and reconnaissance. In this paper, we
propose a novel approach to obfuscating IoT device banners (BANADV) based on
adversarial examples. The key idea is to explore the susceptibility of
fingerprinting techniques to a slight perturbation of an IoT device banner. By
modifying device banners, BANADV disrupts the collection of IoT device
profiles. To validate the efficacy of BANADV, we conduct a set of experiments.
Our evaluation results show that adversarial examples can spoof
state-of-the-art fingerprinting techniques, including learning- and
matching-based approaches. We further provide a detailed analysis of the
weakness of learning-based/matching-based fingerprints to carefully crafted
samples. Overall, the innovations of BANADV lie in three aspects: (1) it
utilizes an IoT-related semantic space and a visual similarity space to locate
available manipulating perturbations of IoT banners; (2) it achieves at least
80\% success rate for spoofing IoT scanning techniques; and (3) it is the first
to utilize adversarial examples of IoT banners in network measurement and
reconnaissance.
