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A Data Streaming Algorithm for Detection of Superpoints With Small Memory Consumption
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DOI：10.1109/LCOMM.2017.2665490.png)
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Abstract
En 中文
A superpoint is a host that communicates with a large number of distinct destinations (sources) within a measurement period. Identifying superpoints is an important and meaningful task for network security and monitoring. To keep up with the line speed in a high-speed network, fast memory is indispensable for detecting superpoints. Moreover, the memory is also expensive and size-limited. In this letter, we propose a new data streaming algorithm for detecting superpoints, called Snare, which can work in tight memory space and yield good performance. Its accuracy and efficiency come from a new data structure snare and the compensation mechanism for the number of lost flows. Theoretical analysis and experimental results show that Snare can detect superpoints accurately and efficiently.
Keywords:
Superpoint
flow compensation
host cardinality
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