Using Classification to Protect the Integrity of Spectrum Measurements in White Space Networks

The emerging paradigm for using the wireless spectrum more efficiently is based on enabling secondary users to exploit white-space frequencies that are not occupied by primary users. A key enabling technology for forming networks over white spaces is distributed spectrum measurements to identify and assess the quality of unused channels. This spectrum availability data is often aggregated at a central base station or database to govern the usage of spectrum. This process is vulnerable to integrity violations if the devices are malicious and misreport spectrum sensing results. In this paper we propose CUSP, a new technique based on machine learning that uses a trusted initial set of signal propagation data in a region as input to build a classifier using Support Vector Machines. The classifier is subsequently used to detect integrity violations. Using classification eliminates the need for arbitrary assumptions about signal propagation models and parameters or thresholds in favor of direct training data. Extensive evaluations using TV transmitter data from the FCC, terrain data from NASA, and house density data from the US Census Bureau for areas in Illinois and Pennsylvania show that our technique is effective against attackers of varying sophistication, while accommodating for regional terrain and shadowing diversity.