[1]何文河[],李陶深[][],黄汝维[][]. 云环境下基于改进BP算法的入侵检测模型[J].计算机技术与发展,2016,26(02):87-90.
　HE Wen-he[],LI Tao-shen[][],HUANG Ru-wei[][]. Intrusion Detection Model Based on Improved BP Algorithm in Cloud Environment[J].,2016,26(02):87-90.
点击复制

 云环境下基于改进BP算法的入侵检测模型()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年02期
页码:
87-90
栏目:
安全与防范
出版日期:
2016-02-10

文章信息/Info

Title:
 Intrusion Detection Model Based on Improved BP Algorithm in Cloud Environment
文章编号:
1673-629X（2016)02-0087-04
作者:
 何文河[1] ; 李陶深[1][2] ; 黄汝维[1][2]
 1.广西大学 计算机与电子信息学院；2. 广西高校并行与分布式计算技术重点实验室
Author(s):
 HE Wen-he[1] ; LI Tao-shen[1][2];  HUANG Ru-wei[1][2]
关键词:
 ; 云安全; 入侵检测; 内核虚拟机; 反向传播神经网络; 粒子群优化算法
Keywords:
 ; cloud security; intrusion detection; KVM; BP neural network; PSO algorithm
分类号:
TP391
文献标志码:
A
摘要:
 随着云计算技术的发展，商业云资源的使用成本越来越低，恶意用户可能利用云平台资源对同驻的虚拟机或者其他云平台实施入侵攻击。针对云服务的入侵攻击主要包括对虚拟机或监视器的攻击和后门通道攻击。针对现有云入侵检测系统仅能检测已知的攻击、对不同虚拟网络模型的兼容性较低、对攻击的变种的检测精度较低等问题，在分析KVM网络模型的基础上，提出一种云环境下基于改进BP算法的入侵检测模型( MBPCIDM)。该模型结合了PSO算法的全局寻优能力和BP算法的梯度下降局部搜索等特点，将PSO算法引入到BP的初始权值与阈值的优化，融入了动量项与自适应学习速率方法，使得BP网络更快收敛，且有效避免了算法陷入局部最优。实验结果表明，所提出的模型平均检出率较高，能为云环境提供入侵检测服务。
Abstract:
 With the development of cloud computing technology,the cost of commercial cloud resources is lower and lower,a malicious user could use the cloud resources in the same virtual machine or other cloud platform to implement intrusion attack. The intrusion attack for cloud service mainly includes the virtual machine or monitor attack and back channel attack. The existing cloud intrusion detection systems can only detect known attacks,cannot be applied to a virtualized environment that has different network models,and the detection accuracy of variant of attack is lower. Based on the analysis of the KVM network structures,an improved intrusion detection model based on BP algorithm in the cloud environment ( MBPCIDM) was proposed. It combines the ability of searching global optimal solution of PSO algorithm and the feature of the gradient descent in local search of BP algorithm. To make the BP network convergence faster and prevent it from falling into local optimum,the momentum and adaptive learning rate method was also used in this paper. The experimental results show that the average detection rate of the proposed model is higher,it can provide intrusion detection services for cloud environ-ments.

相似文献/References:

[1]黄秀丽.解读云立方体模型[J].计算机技术与发展,2012,(03):245.
　HUANG Xiu-li.Analysis of Cloud Cube Model[J].,2012,(02):245.
[2]朱圣才.基于Azure的云安全研究[J].计算机技术与发展,2013,(12):143.
　ZHU Sheng-cai.Research on Cloud Computing Security Based on Windows Azure[J].,2013,(02):143.
[3]王茜,朱志祥,葛新,等.应用于云计算中心的虚拟主机安全防护系统[J].计算机技术与发展,2014,24(03):134.
　WANG Qian[],ZHU Zhi-xiang[],GE Xin[],et al.A Virtual Host Protection System Applying to Cloud Computing Center[J].,2014,24(02):134.
[4]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(02):1.
[5]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(02):5.
[6]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(02):13.
[7]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(02):21.
[8]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(02):25.
[9]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(02):29.
[10]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(02):34.
[11]谭天,袁嵩,肖洁. 云计算安全问题研究[J].计算机技术与发展,2016,26(09):124.
　TAN Tian,YUAN Song,XIAO Jie. Research on Security Issues of Cloud Computing[J].,2016,26(02):124.

更新日期/Last Update: 2016-04-15