Distributed attacks have reportedly caused the most serious losses in recent years. Here, distributed attacks means those attacks conducted collaboratively by multiple hosts. How to detect distributed attacks has become one of the most important topics in the cyber security community. Many detection methods have been proposed, each of which, however, has its own weak points. For example, detection performance of information theory based methods strongly depends on the information theoretic measures and signature-based methods suffer from the fact that they can deal with neither new kinds of attacks nor new variants of existing attacks. Recently, behavior-based method has been attracting great attentions from many researchers and developers and it is thought as the most promising one. In behavior-based approaches, normal behavior modes are learned/extracted from past traffic data of the monitored network and are used to recognize anomalies in the future detection. In this paper, we explain how to implement an online behavior-based engine for detecting distributed cyber-attacks. Detection cases of our engine are also introduced and some actual attacks/incidents have been captured by our detection engine.