Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
PATRICIA—Practical Algorithm To Retrieve Information Coded in Alphanumeric
Journal of the ACM (JACM)
On network-aware clustering of Web clients
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
SVM learning of IP address structure for latency prediction
Proceedings of the 2006 SIGCOMM workshop on Mining network data
iPlane: an information plane for distributed services
OSDI '06 Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7
An adaptive anomaly detector for worm detection
SYSML'07 Proceedings of the 2nd USENIX workshop on Tackling computer systems problems with machine learning techniques
Statistical learning in network architecture
Statistical learning in network architecture
Primitives for active internet topology mapping: toward high-frequency characterization
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Internet-scale visualization and detection of performance events
USENIXATC'11 Proceedings of the 2011 USENIX conference on USENIX annual technical conference
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We pose partitioning a b-bit Internet Protocol (IP) address space as a supervised learning task. Given (IP, property) labeled training data, we develop an IPspecific 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-naïve 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.