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Identification of significant patterns in network traffic, such as IPs or flows that contribute large volume (heavy hitters) or those that introduce large changes of volume (heavy changers), has many applications in accounting and network anomaly detection. As network speed and the number of flows grow rapidly, identifying heavy hitters/changers by tracking per-IP or per-flow statistics becomes infeasible due to both the computational overhead and memory requirements. In this paper, the authors propose SeqHash, a novel sequential hashing scheme that supports fast and accurate recovery of heavy hitters/changers, while requiring memory just slightly higher than the theoretical lower bound.
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