Credential theft is a network intrusion vector that subverts traditional defenses of a campus network, with a malicious login being the act of an attacker using those stolen credentials to access the target network. Historically, this approach is simple for an attacker to conduct and hard for a defender to detect. Alternative mitigation strategies require an in depth view of the network hosts, an untenable proposition in a campus network. We introduce a method of spatial augmentation of login events, creating a user and source IP trajectory for each event. These location mappings, built using user wireless activity and network state information, provide features needed for login classification. From this, we design and build a real time data collection, augmentation, and classification system for generating alerts on malicious events. With a relational database for data processing and a trained weighted random forests ensemble classifier, generated alerts are both timely and few enough to allow human analyst review of all generated events. We evaluate this design for three levels of attacker ability with a defined threat model. We evaluate our approach with a proof of concept system on weeks of live data collected from the Virginia Tech campus, under an IRB approved research protocol.