Computational intelligence has figured prominently in many solutions to the network intrusion detection problem since the 1990s. This prominence and popularity has continued in the contributions of the recent past. These contributions present the success and potential of computational intelligence in network intrusion detection systems for tasks such as feature selection, signature generation, anomaly detection, classification, and clustering. This paper reviews these contributions categorized in the sub-areas of soft computing, machine learning, artificial immune systems, and agentbased systems.