We use the findings from the decision tree for each class to get a final conclusion. The Decision Tree illustrates the decision constraints for each cluster by learning the subgroups inside this cluster. In each cluster, we create decision structures with algorithms from the decision tree C4.5, indicating a density area of typical or abnormal cases. K‐Means is the first clustering method for separating training into K clusters with a similarity in Euclidean distance.
In this research, we present a method of identification of anomalies with “K‐means + C4.5,” the method of cascading k‐means clustering and the decision tree method C4.5, for classifying anomalous and typical computer network operations. The system's behavior and flashing systems are monitoring important anomalies in the anomaly detection system (ADS). The intrusion detection system in the network is designed to detect attacks or malicious activity in a high‐detection network while keeping a low false alarm rate. Intrusion in a network environment poses a severe security risk. In the framework of suggestions and information dissemination on the blogosphere, we are considering cascades of information. As such technique is transmitted across a social network, broad adoption can occur. Cascades of information are a phenomena where individuals take a new action or thought because of their influence.
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