Network surveillance, i.e., the detection of anomalous behaviour in communications in a network, has become an important issue in recent years. In this field, techniques of statistical process monitoring, especially control charts, have been frequently applied to monitor the probability of existing connections between the members (nodes) of such a network. As another approach, monitoring the number of communications between the nodes is also an important way to identify network anomalies. Previous works have focused on conventional statistical control charts, but there are no machine learning approaches, although the performance of machine learning based control charts has been shown to be excellent in several other applications. This paper aims to develop machine learning based control charts for network surveillance problems in the case of monitoring the number of communications between the nodes. The results of extensive Monte Carlo simulations reveal the superiority of the proposed method over conventional competitors.