Voice traffic termination fraud, often referred to as Subscriber Identity Module box (SIMbox) fraud, is a common illegal practice on mobile networks. As a result, cellular operators around the globe lose billions annually. Moreover, SIMboxes compromise the cellular network infrastructure by overloading local base stations serving these devices. This paper analyzes the fraudulent traffic from SIMboxes operating with a large number of SIM cards. It processes hundreds of millions of anonymized voice call detail records (CDRs) from one of the main cellular operators in the United States. In addition to overloading voice traffic, fraudulent SIMboxes are observed to have static physical locations and to generate disproportionately large volume of outgoing calls. Based on these observations, novel classifiers for fraudulent SIMbox detection in mobility networks are proposed. Their outputs are optimally fused to increase the detection rate. The operator's fraud department confirmed that the algorithm succeeds in detecting new fraudulent SIMboxes.