  Super point is a special kind of host whose cardinality, the number of
contacting hosts in a certain period, is bigger than a threshold. Super point
cardinality estimation plays important roles in network field. This paper
proposes a super point cardinality estimation algorithm under sliding time
window. To maintain the state of previous hosts with few updating operations, a
novel counter, asynchronous time stamp (AT), is proposed. For a sliding time
window containing k time slices, AT only needs to be updated every k time
slices at the cost of 1 more bit than a previous state-of-art counter which
requires $log_2(k+1)$ bits but updates every time slice. Fewer updating
operations mean that more AT could be contained to acquire higher accuracy in
real-time. This paper also devises a novel reversible hash function scheme to
restore super point from a pool of AT. Experiments on several real-world
network traffic illustrate that the algorithm proposed in this paper could
detect super points and estimate their cardinalities under sliding time window
in real time.
