Abstract
The objective of this article is to explore the possibilities of identifying network attacks using cluster analysis methods. Density-based spatial clustering of applications with noise (DBSCAN) and the ordering points to identify the clustering structure (OPTICS) algorithm have been implemented on data representing a simulated network. The findings reveal the most effective algorithmic combinations for distinguishing between regular connections and data associated with network intrusions.
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