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Anomaly detection, which is an important task in any Network Intrusion Detection System (NIDS), enables detection of known as well as unknown attacks. Anomaly detection using outlier identification is a successful network anomaly identification technique. In this paper, the authors describe NADO (Network Anomaly Detection using Outlier Approach), an effective outlier technique for detection of anomalies in networks. It initially clusters the normal data using a variant of the k-means clustering technique for high dimensional data. Then it calculates the reference point from each cluster and builds profiles for each cluster.
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