A precursor to many attacks on networks is often a reconnaissance operation, more commonly referred to as a scan. Despite the vast amount of attention focused on methods for scan detection, the state-of-the-art methods suffer from high rate of false alarms and low rate of scan detection. In this paper, we formalize the problem of scan detection as a data mining problem. We show how the network traffic data sets can be converted into a data set that is appropriate for running off-the-shelf classifiers on. Our method successfully demonstrates that data mining models can encapsulate expert knowledge to create an adaptable algorithm that can substantially outperform state-of-the-art methods for scan detection in both coverage and precision.