This document proposes a method to detect and localize malicious users on a WiFi network. It involves using received signal strength (RSS) spatial correlation data, which is difficult to falsify and does not rely on cryptography. The RSS readings from wireless nodes are used to detect spoofing attacks in signal space and identify the number of attackers in physical space. Support vector machines are explored to improve accuracy of determining the number of attackers after training data is available. Clustering algorithms are developed to classify RSS readings from different physical locations versus variations, helping to accurately localize multiple adversaries.