heal.abstract |
In this paper an enhanced anomaly detection approach based on the fusion of data gathered from various monitors spread throughout a wide area network is introduced. The proposed approach is based on the application of principal component analysis on multi-metric-multi-link data, and provides an efficient and unified way of taking into account the combined effect of the correlated observed data, for anomaly detection purposes. It actually introduces a generalized anomaly detection methodology, capable of detecting not only volume based anomalies but also a much wider range of classes of anomalies, such as the ones that may result in alterations in traffic composition or traffic paths. The performance of the proposed multi-metric-multi-link anomaly detection approach is evaluated via simulation, and is compared against the corresponding techniques that are based on the single-metric analysis. Finally, its operational effectiveness is demonstrated in a realistic environment using real data collected from the core routers of the Greek research and technology network (GRNET). Copyright (C) 2008 John Wiley & Sons, Ltd. |
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