[1]蒋鸿玲,邵秀丽.基于神经网络的僵尸网络检测[J].智能系统学报,2013,8(02):113-118.[doi:10.3969/j.issn.1673-4785.201210055]
 JIANG Hongling,SHAO Xiuli.Botnet detection algorithm based on neural network[J].CAAI Transactions on Intelligent Systems,2013,8(02):113-118.[doi:10.3969/j.issn.1673-4785.201210055]
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基于神经网络的僵尸网络检测(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第8卷
期数:
2013年02期
页码:
113-118
栏目:
出版日期:
2013-04-25

文章信息/Info

Title:
Botnet detection algorithm based on neural network
文章编号:
1673-4785(2013)02-0113-06
作者:
蒋鸿玲邵秀丽
南开大学 信息技术科学学院,天津 300071
Author(s):
JIANG Hongling SHAO Xiuli
College of Information Technical Science, Nankai University, Tianjin 300071, China
关键词:
僵尸网络BP神经网络特征向量网络流量检测算法
Keywords:
botnet BP neural network feature vector network traffic detection algorithm
分类号:
TP393
DOI:
10.3969/j.issn.1673-4785.201210055
文献标志码:
A
摘要:
目前主流的僵尸网络检测方法主要利用网络流量分析技术,这往往需要数据包的内部信息,或者依赖于外部系统提供的信息或僵尸主机的恶意行为,并且大多数方法不能自动存储僵尸网络的流量特征,不具有联想记忆功能.为此提出了一种基于BP神经网络的僵尸网络检测方法,通过大量的僵尸网络和正常流量样本训练BP神经网络分类器,使其学会辨认僵尸网络的流量,自动记忆僵尸流量特征,从而有效检测出被感染的主机.该神经网络分类器以主机对为分析对象,提取2个主机间通信的流量特征,将主机对的特征向量作为输入,有效地区分出正常主机和僵尸主机.实验表明,该方法的检测率达到99%,误报率在1%以下,具有良好的性能.
Abstract:
The most current botnet detection algorithm are typically based on network traffic analyzing technologies that usually need packet payload. The botnet detection algorithm also relies on information obtained by external systems or malicious behaviors of bots that do not automatically store the features of botnet traffic and do not have the ability of associative memory. As a result, this paper proposes a botnet detection algorithm based on BP neural network which trains the BP neural network classifier through a lot of botnet and normal traffic samples and allows it to learn how to identify botnet traffic and automatically remember the features of botnet traffic and therefore, detect the infected hosts effectively. The neural network classifier takes the host-pairs as analysis objects, extracts the traffic features of communications between two hosts and takes the feature vectors of host-pairs as input, thus, effectively distinguishing the normal hosts and bots. The experimental results show that the detection rate of our algorithm can achieve to 99% and the false positive rate is below 1% and the algorithm has a good performance.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2012-10-26.
网络出版日期:2013-04-09.
基金项目:国家科技支撑计划基金资助项目(2012BAF12B00);天津市重点基金资助项目(11jczdjc28100).
通信作者:邵秀丽.
E-mail:shaoxl@nankai.edu.cn.
作者简介:
蒋鸿玲,女,1986年生,博士研究生,主要研究方向为网络安全与云计算等,发表学术论文7篇.
邵秀丽,女,1963年生,教授,博士生导师,主要研究方向为云计算与软件工程等,发表学术论文80余篇.
更新日期/Last Update: 2013-05-26