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- W2401649681 abstract "This dissertation deals with the detection of Internet traffic anomalies in flow-level measurement data as well as the identification of their causes. The detection of traffic anomalies is based on time series of different traffic metrics. As anomaly detection methods, various combinations of single-metric and multi-metric residual generation and change detection techniques are evaluated and compared. Most of the detected anomalies which are of interest for the network administrator go back to a few types of causes, such as scanning activities and brute-force password guessing. In order to identify these causes and the involved hosts, identification algorithms are presented which search the measured flows for characteristic traffic patterns." @default.
- W2401649681 created "2016-06-24" @default.
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- W2401649681 date "2010-01-01" @default.
- W2401649681 modified "2023-10-12" @default.
- W2401649681 title "Traffic Anomaly Detection and Cause Identification Using Flow-Level Measurements" @default.
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