Mobile Network Fraud Analysis and Detection: Over the last years, wireless devices connected to the mobile network have been leveraged for various fraudulent and illegal activities such as massive dissemination of spam messages and arrangement of elaborated voice call bypassing schemes. Besides causing the economic loss to cellular operators, fraudsters degrade the local service where they operate. Often, cells are overloaded, and voice calls fraudulently re-routed have poor quality, which results in customer dissatisfaction. The large amount of daily cellular traffic and continuously increasing number of mobile devices connecting to the network make detecting fraudulent activities extremely challenging. This talk will introduce the huge potential that network-based machine learning algorithms have in detecting fraudulent activities in cellular networks. Specifically, it will demonstrate the effectiveness of combining predictions of multiple classifiers in detecting SMS spam and SIMbox call bypass fraud in hundreds of millions of anonymized SMS and voice call detail records from one of the main cellular operators in the United States.