Jagatheesan Kunasaikaran,Roslan Ismail,Abdul Rahim Ahmad,



Clustering methods,Intrusion detection,Network security,


The escalating number of novel network attacks warrants an approach where network data is processed in real-time for anomaly detection. Clustering is one of the foremost unsupervised learning algorithms in this domain that can detect outliers without prior knowledge of the data. However, cluster analysis precludes with it many challenges that need to be overcome for it to be adapted for real-time computation. This research paper outlines these challenges and the possible solutions to mitigate these challenges. We have also explored on a brief overview of clustering algorithms to give a high-level idea of cluster analysis.


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