Low-complexity detection of potential network anomalies using intermediate-stage processing
Abstract:
In an embodiment, a computer implemented method receives flow data for a network flows. The method extracts a tuple from the flow data and calculates long-term and short-term trends based at least in part on the tuple. The long-term and short-term trends are compared to determine whether a potential network anomaly exists. If a potential network anomaly does exist, the method initiates a heavy hitter detection algorithm. The method forms a low-complexity intermediate stage of processing that enables a high-complexity heavy hitter detection algorithm to execute when heavy hitters are likely to be detected.
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