The Impact of Distance Metrics on K-means Clustering Algorithm Using in Network Intrusion Detection Data

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Provided by: International Journal of Computer Networks and Communications Security (IJCNCS)
Topic: Security
Format: PDF
A Network Intrusion Detection System (NIDS) can detect suspicious activities that aimed to harm the network. Since, NIDS help the authors to keep the networks safer many researchers are motivated to propose more accurate NIDS. K-means clustering algorithm is a distance-based algorithm which widely used in IDS research area. This paper aimed to evaluate the impact of Euclidean and Manhattan distance metrics on K-means algorithm using for clustering KDD cup99 intrusion detection data. Experimental results indicate that Manhattan distance metric performs better in terms of performance evaluation metrics than Euclidean distance metric.
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