###
DOI:
Journal of Software:2007.18(12):3060-3070

高速网络监控中大流量对象的提取
王风宇,云晓春,王晓峰,王勇
(中国科学院,计算技术研究所,信息智能与信息安全研究中心,北京,100080;山东大学,计算机科学与技术学院,济南,250101;中国科学院,研究生院,北京,100049;哈尔滨工业大学,计算机网络与信息安全技术研究中心,黑龙江,哈尔滨,150001)
Identifying Heavy Hitters in High-Speed Network Monitoring
WANG Feng-Yu,YUN Xiao-Chun,WANG Xiao-Feng,WANG Yong
()
Abstract
Chart / table
Reference
Similar Articles
Article :Browse 3760   Download 3478
Received:May 16, 2006    Revised:November 13, 2006
> 中文摘要: 在高速网络环境下,由于受计算及存储资源的限制,及时、准确地提取大流量对象对于检测大规模网络安全事件具有重要意义.结合LRU淘汰机制和LEAST淘汰机制,建立了基于二级淘汰机制的网络大流量对象提取算法(LRU&LEAST replacement,简称LLR),两种淘汰机制相互弥补不足,较大地提高了算法的准确性.由于算法占用存储空间较少,从而可以在有限的SRAM空间中更快地处理流量信息.该算法在网络数据量增加的情况下不必增加存储空间,具有很好的可扩展性.
Abstract:Due to the deficiency of traffic measurement capability in high-speed network,it's valuable for detecting large-scale network security incident to identify heavy hitters precisely in time.An algorithm of identifying heavy hitters based on two-level replacement mechanism is proposed in this paper.In this algorithm, LRU replacement and LEAST replacement are combined together to improve its accuracy.The heavy hitters can be identified accurately in small constant memory space,so the data can be treated more rapidly in limited space of SRAM.It's unnecessary to provide more memory space for more network data,so the algorithm is scalable.
文章编号:     中图分类号:    文献标志码:
基金项目:Supported by the National Natural Science Foundation of China under Grant No.60573134 (国家自然科学基金); the Program for New Century Excellent Talents in University of China (新世纪优秀人才支持计划) Supported by the National Natural Science Foundation of China under Grant No.60573134 (国家自然科学基金); the Program for New Century Excellent Talents in University of China (新世纪优秀人才支持计划)
Foundation items:
Reference text:

王风宇,云晓春,王晓峰,王勇.高速网络监控中大流量对象的提取.软件学报,2007,18(12):3060-3070

WANG Feng-Yu,YUN Xiao-Chun,WANG Xiao-Feng,WANG Yong.Identifying Heavy Hitters in High-Speed Network Monitoring.Journal of Software,2007,18(12):3060-3070