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Finding Critical Infrastructure Using Wardriving Data

Abstract

As the need for convenience and accessibility of technology grows, so does the number of wireless devices. Users of these devices can range from the average individual to corporations to the government. On one hand, these wireless devices can afford their users with an easier experience, but on the other hand, they can become entry points to malicious attacks. While there is extensive research into the security of wireless devices on a small scale, there lacks an understanding of it on a larger scale: what infrastructures are vulnerable, where these vulnerabilities are located, and what devices make the infrastructure vulnerable. In order to get a better understanding of these wireless-capable infrastructures, we first need data of these wireless devices. One readily available way to obtain this data is through wardriving. However, wardriving data is extremely large and noisy; it contains all scanned wireless data, including devices not related to any infrastructure. Thus, it can be hard to get any relevant devices out of the data. In this work, we try to understand the viability of identifying infrastructure-related devices using wardriving data. We find that it is possible through using a variety of cleaning, clustering, and filtering heuristics to remove noisy data. We then discuss the devices that we found and consider the implications and impact of a few of them.

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