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Sliding super point is a special host defined under sliding time window withwhich there are huge other hosts contact. It plays important roles in networksecurity and management. But how to detect them in real time from nowadayshigh-speed network which contains several distributed routers is a hard task.Distributed sliding super point detection requires an algorithm that canestimate the number of contacting hosts incrementally, scan packets faster thantheir flowing speed and reconstruct sliding super point at the end of a timeperiod. But no existing algorithm satisfies these three requirementssimultaneously. To solve this problem, this paper firstly proposed adistributed sliding super point detection algorithm running on GPU. Theadvantage of this algorithm comes from a novel sliding estimator, which canestimate contacting host number incrementally under a sliding window, and a setof reversible hash functions, by which sliding super points could be regainedwithout storing additional data such as IP list. There are two main proceduresin this algorithm: packets scanning and sliding super points reconstruction.Both could run parallel without any data reading conflict. When deployed on alow cost GPU, this algorithm could deal with traffic with bandwidth as high as680 Gb/s. A real world core network traffic is used to evaluate the performanceof this sliding super point detection algorithm on a cheap GPU, Nvidia GTX950with 4 GB graphic memory. Experiments comparing with other algorithms underdiscrete time window show that this algorithm has the highest accuracy. Undersliding time widow, this algorithm has the same performance as in discrete timewindow, where no other algorithms can work.

Regain Sliding super point from distributed edge routers by GPU