摘要
针对被管网大流量条件下藏区Web站点流识别算法准确性低、鲁棒性差等问题,提出一种基于布隆过滤BF(bloom filter)的藏区Web站点流量识别方法。给出能够描述藏区Web站点流的特征字段,形成关键字集合,并映射为BF中的位数组;在给定假阳率的情况下,利用Hash函数对被管网中数据包的相应特征字段进行Hash映射操作,识别该包是否为藏区Web站点流量的网络包。实验结果表明,该方法呈现了较高的准确性,识别率保持在92.3%以上,在网络流量较大时仍表现出较强的鲁棒性。
Aiming at the problem of poor accuracy and robustness of current Tibetan Website flow identification algorithm under the condition of high flow rate of the monitored network,a Tibetan Website traffic recognition method based on Bloom filter(TWTBF)was proposed.The packet feature fields of Tibetan Website traffic were extracted to form a keyword set which were then mapped to bit array of Bloom filter(BF).In the case of given some false positive rates,the corresponding feature fields of packet in monitored network were computed and mapped using Hash function,which decided whether or not this packet belonged to Tibetan Website traffic.Experimental results show that,the proposed method not only shows high accuracy with a correct identification rate of 92.3%above,but also presents strong robustness under the heavy network traffic.
作者
郭晓军
孙海霞
张国梁
GUO Xiao-jun;SUN Hai-xia;ZHANG Guo-liang(School of Information Engineering,Xizang Minzu University,Xianyang 712082,China;School of Computer Science and Engineering,Southeast University,Nanjing 211189,China;Xizang Key Laboratory of Optical Information Processing and Visualization Technology,Xizang Minzu University,Xianyang 712082,China)
出处
《计算机工程与设计》
北大核心
2018年第2期365-369,共5页
Computer Engineering and Design
基金
2017年西藏民族大学"青年学人培育计划"科研基金项目(17MDQP05)
西藏自治区高校青年教师创新支持计划基金项目(QCZ2016-41)
藏区网络空间安全与舆情智能监管科研创新团队建设基金项目
西藏民族大学2016教改基金项目