  Sliding super point is a special host defined under sliding time window with
which there are huge other hosts contact. It plays important roles in network
security and management. But how to detect them in real time from nowadays
high-speed network which contains several distributed routers is a hard task.
Distributed sliding super point detection requires an algorithm that can
estimate the number of contacting hosts incrementally, scan packets faster than
their flowing speed and reconstruct sliding super point at the end of a time
period. But no existing algorithm satisfies these three requirements
simultaneously. To solve this problem, this paper firstly proposed a
distributed sliding super point detection algorithm running on GPU. The
advantage of this algorithm comes from a novel sliding estimator, which can
estimate contacting host number incrementally under a sliding window, and a set
of reversible hash functions, by which sliding super points could be regained
without storing additional data such as IP list. There are two main procedures
in this algorithm: packets scanning and sliding super points reconstruction.
Both could run parallel without any data reading conflict. When deployed on a
low cost GPU, this algorithm could deal with traffic with bandwidth as high as
680 Gb/s. A real world core network traffic is used to evaluate the performance
of this sliding super point detection algorithm on a cheap GPU, Nvidia GTX950
with 4 GB graphic memory. Experiments comparing with other algorithms under
discrete time window show that this algorithm has the highest accuracy. Under
sliding time widow, this algorithm has the same performance as in discrete time
window, where no other algorithms can work.
