We present a real-time monitoring system for detecting anomalous network events using the entropy. The entropy accounts for the effects of disorder in the system. When an abnormal factor arises to agitate the current system the entropy must show an abrupt change. In this paper we deliberately model the Internet to measure the entropy. Packets flowing between these two networks may incur to sustain the current value. In the proposed system we keep track of the value of entropy in time to pinpoint the sudden changes in the value. The time-series data of entropy are transformed into the two-dimensional domains to help visually inspect the activities on the network. We examine the system using network traffic traces containing notorious worms and DoS attacks on the testbed. Furthermore, we compare our proposed system of time series forecasting method, such as EWMA, holt-winters, and PCA in terms of sensitive. The result suggests that our approach be able to detect anomalies with the fairly high accuracy. Our contributions are two folds: (1) highly sensitive detection of anomalies and (2) visualization of network activities to alert anomalies.