This paper proposes methods for detecting spoofing attacks in wireless networks, determining the number of attackers when multiple adversaries spoof the same node, and localizing the positions of multiple attackers. It uses the spatial correlation of received signal strength inherent in wireless transmissions to detect spoofing attacks. Cluster-based mechanisms and support vector machines are developed to determine the number of attackers by formulating it as a multiclass detection problem. Experimental results using WiFi and ZigBee networks in two buildings show the methods can achieve over 90% accuracy in determining the number of attackers and provide strong evidence of accurately localizing multiple adversaries.