Traffic identification algorithm based on improved LRU
S Wen, D Qin, T Lv, L Ge, X Yang - 2020 7th IEEE International …, 2020 - ieeexplore.ieee.org
S Wen, D Qin, T Lv, L Ge, X Yang
2020 7th IEEE International Conference on Cyber Security and Cloud …, 2020•ieeexplore.ieee.orgIt is very important to be able to identify large traffic accurately and quickly in high-speed
networks. Existing traffic classification algorithms have many shortcomings. In this paper, a
traffic classification algorithm LRU_DCBF is proposed based on the characteristics of the
least recently used (LRU) and the counting bloom filter (CBF). Based on this algorithm, the
mapping range of hash function is improved, so that each hash function has its own space
without mutual interference. The results of analysis and experiment show that the false alarm …
networks. Existing traffic classification algorithms have many shortcomings. In this paper, a
traffic classification algorithm LRU_DCBF is proposed based on the characteristics of the
least recently used (LRU) and the counting bloom filter (CBF). Based on this algorithm, the
mapping range of hash function is improved, so that each hash function has its own space
without mutual interference. The results of analysis and experiment show that the false alarm …
It is very important to be able to identify large traffic accurately and quickly in high-speed networks. Existing traffic classification algorithms have many shortcomings. In this paper, a traffic classification algorithm LRU_DCBF is proposed based on the characteristics of the least recently used (LRU) and the counting bloom filter (CBF). Based on this algorithm, the mapping range of hash function is improved, so that each hash function has its own space without mutual interference. The results of analysis and experiment show that the false alarm rate and false alarm rate are reduced and the storage space is reduced.
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