Network modeling and analysis are essential tools to understand complex systems in various contexts. In many applications, it is crucial to identify anomalous behavior among actors in a network that evolves over time. For example, on online social networks, a sudden surge in communications might indicate illegal activities such as fraud or collusion. This paper proposes an extension of statistical network monitoring to develop a surveillance system capable of detecting structural changes. The proposed methodology is tested on the email exchange network of Enron Corporation, a major U.S. energy company involved in one of the biggest financial scandals in history.