| Principle Components and Importance Ranking of Distributed Anomalies (2004) | |||||||||||||||||
Abstract | |||||||||||||||||
| Correlations between locally averaged host observations, at different times and places, hint at information about the associations between the hosts in a network. These smoothed, pseudo-continuous time-series imply relationships with entities in the wider environment. For anomaly detection, mining this information might provide a valuable source of observational experience for determining comparative anomalies or rejecting false anomalies. The di#culties with distributed analysis lie in collating the distributed data and in comparing observables on di#erent hosts, in di#erent frames of reference. In the present work, we examine two methods (Principle Component Analysis and Eigenvector Centrality) that shed light on the usefulness of comparing data destined for di#erent locations in a network. | |||||||||||||||||
Details der Publikation | |||||||||||||||||
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