An Internet Protocol Address Clustering Algorithm

Robert Beverly and Karen Sollins.
Proceedings of USENIX Tackling Computer Systems Problems with Machine Lea rning Techniques (SysML 2008),
San Diego, CA, December 2008.

We pose partitioning a $b$-bit Internet Protocol (IP) address space as a supervised learning task. Given (\emph{IP, property}) labeled training data, we develop an IP-specific clustering algorithm that provides accurate predictions for unknown addresses in $O(b)$ run time. Our method offers a natural means to penalize model complexity, limit memory consumption, and is amenable to a non-stationary environment. Against a live Internet latency data set, the algorithm outperforms IP-na\"ive learning methods and is fast in practice. Finally, we show the model's ability to detect structural and temporal changes, a crucial step in learning amid Internet dynamics.

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