Abstract: In high-speed network environments,super-spreaders are defined as hosts or devices with a large number of connections.Accurate super spreader detection is crucial for many applications such as network monitoring,security analysis,and traffic management.Invertible algorithms based on sketches have received extensive attention due to their excellent memory efficiency and the ability to directly recover the super spreader identity from the internal structure.According to application interests,the packets sent or received from the same host or device are abstracted as a flow.The flow distribution in high-speed network is usually highly skewed,only a few flows are large flows,and the vast majority are small flows.However,the memory structure design of the existing research cannot efficiently adapt to the highly skewed flow distribution,which makes the memory resource utilization low.Therefore,this paper designs an adaptive sampling based super spreader detection algorithm AS-SSD.The algorithm proposes an adaptive sampling strategy based on register sharing,which makes up for the above shortcomings.AS-SSD first maps the elements of all flows into a register array.Small flows only consume a few registers,while larger flows consume more,which adapts to the skewed flow distribution.Then,AS-SSD uses an adaptive sampling strategy to dynamically adjust the element sampling probability of flows of different sizes,which reduces the occupation of registers by large flows and further improves the utilization efficiency of memory resources under the premise of ensuring accuracy.Experimental evaluations show that compared with the previous work,AS-SSD shows higher detection accuracy in the super spreader detection task while maintaining high throughput processing capability.Compared with the most advanced algorithms,it can increase the F1 value by more than 0.609.

Key words: Traffic measurement, Super spreader detection, High-speed network, Adaptive sampling, Sketch