Intrusions pose a serious securing risk in a network environment. Network intrusion detection system aims to identify attacks or malicious activity in a network with a high detection rate while maintaining a low false alarm rate. Anomaly detection systems (ADS) monitor the behaviour of a system and flag significant deviations from the normal activity as anomalies. In this paper, we propose an anomaly detection method using "K-Means + C4.5", a method to cascade k-Means clustering and the C4.5 decision tree methods for classifying anomalous and normal activities in a computer network. The k-Means clustering method is first used to partition the training instances into k clusters using Euclidean distance similarity. On each cluster, representing a density region of normal or anomaly instances, we build decision trees using C4.5 decision tree algorithm. The decision tree on each cluster refines the decision boundaries by learning the subgroups within the cluster. To obtain a final conclusion we exploit the results derived from the decision tree on each cluster.