[1]秦华旺,戴跃伟,王执铨,等.一种基于改进神经网络的入侵容忍系统模型[J].南京理工大学学报(自然科学版),2008,(05):628-631.
 QIN Hua-wang,DAI Yue-wei,WANG Zhi-quan.Model of Intrusion Tolerant System Based on Improved Neural Networks[J].Journal of Nanjing University of Science and Technology,2008,(05):628-631.
点击复制

一种基于改进神经网络的入侵容忍系统模型
分享到:

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
期数:
2008年05期
页码:
628-631
栏目:
出版日期:
2008-10-30

文章信息/Info

Title:
Model of Intrusion Tolerant System Based on Improved Neural Networks
作者:
秦华旺;戴跃伟;王执铨;
南京理工大学自动化学院, 江苏南京210094
Author(s):
QIN Hua-wangDAI Yue-weiWANG Zhi-quan
School of Automation,NUST,Nanjing 210094,China
关键词:
入侵容忍 神经网络 反向传播网络
Keywords:
intrusion tolerance neural networks back propagation network
分类号:
TP393.08
摘要:
该文通过对资源和控制两个属性的分析,将入侵容忍系统的运行状态进行了抽象分类,给出了系统不同运行状态下的相应安全机制;利用改进的反向传播(BP)神经网络对入侵容忍系统建模,给出了BP网络的输入和输出节点以及基于该BP网络的系统工作原理,用实例描述了该系统的一个典型的入侵容忍过程;实验结果表明,该系统对入侵具有较好的检测和容忍能力。
Abstract:
The work states of intrusion tolerant system are classified abstractly through analyzing the two attributes: resource and control.The corresponding safe mechanisms in different work states of the system are given.A model of intrusion tolerant system based on improved back propagation(BP) neural networks is established,the input and output nodes of the BP network are given,and the work principle of the system based on the BP network is described.A typical intrusion tolerant process of the system is described through an example.The experimental results show that the system has a good ability of detecting and bearing the intrusion.

参考文献/References:

[1] 彭文灵, 王丽娜, 张焕国, 等. 基于角色访问控制的入侵 容忍机制研究[ J]. 电子学报, 2005, 33( 1): 91- 95.
[2] 郭渊博, 马建峰. 入侵容忍的国内外研究现状及所 存在的问题分析[ J]. 信息安全与保密通信, 2005, 7 ( 1): 337- 341.
[3] C astro M, Liskov B. Proactive recovery in a Byzantine- fau lt- to lerant system [ J]. Operating System s Design and Imp,l 2001, 22( 4): 273- 288.
[4] K re id lO P, FrazierT M. Feedback contro l applied to surv ivab ility: a host-based automa tic de fense sy stem [ J] . IEEE T ransactions on Re liab ility, 2004, 53( 1): 148- 166.
[5] Go seva-Popsto janovaK, W ang F, W ang R. Cha racte rizing in trusion tolerant sy stem s using a state transition m ode l[ J]. DARPA Inform a tion Su rv ivability Con fe rence and Exposition, 2001, 2( 1): 211- 221.
[6] 荆继武, 冯登国. 一种入侵容忍的CA方案[ J] . 软 件学报, 2002, 13( 8) : 1 417- 1 422.
[7] 黄耀宇, 李从东. 基于人工神经网络的煤矿安全评 估模型研究[ J]. 工业工程, 2007, 10( 1) : 112- 115.
[8] 周志勇, 唐家益. 基于安全域和可信基的网络容侵系 统模型[ J]. 微计算机信息, 2003, 19( 11): 108- 109.
[9] 崔竞松, 王丽娜, 张焕国, 等. 一种并行容侵系统研 究模型) ) ) RC模型[ J]. 计算机学报, 2004, 27( 4): 500- 506.
[10] 陈科, 许家珆, 程永新. 基于免疫算法和神经网络的 新型抗体网络[ J]. 电子科技大学学报, 2006, 35 ( 5): 804- 840.
[11] 杨忠林, 张静, 田培根. 改进的BP网络及其在电路 故障诊断中的应用[ J]. 船舶电子工程, 2006, 26 ( 6): 103- 106.

相似文献/References:

[1]陈机林,王力,高强,等.爆破扫雷器电液伺服系统建模[J].南京理工大学学报(自然科学版),2012,36(04):645.
 CHEN Ji-lin,WANG Li,GAO Qiang,et al.Modeling of Electro-hydraulic Servo System of Explosive Sweeper Mine Device[J].Journal of Nanjing University of Science and Technology,2012,36(05):645.
[2]高强,金勇,侯远龙,等.某扫雷车扫雷犁电液伺服系统辨识与控制[J].南京理工大学学报(自然科学版),2012,36(02):238.
 GAO Qiang,JIN Yong,HOU Yuan-long,et al.Modeling and Control for Mine Sweeping Plough Electro-hydraulic Servo System of Certain Mine-clearing Vehicle[J].Journal of Nanjing University of Science and Technology,2012,36(05):238.
[3]余华,黄程韦,张潇丹,等.混合蛙跳算法神经网络及其在语音情感识别中的应用[J].南京理工大学学报(自然科学版),2011,(05):659.
 YU Hua,HUANG Cheng-wei,ZHANG Xiao-dan,et al.Shuffled Frog-leaping Algorithm Based Neural Network and Its Application in Speech Emotion Recognition[J].Journal of Nanjing University of Science and Technology,2011,(05):659.
[4]林棻,赵又群.汽车侧偏角估计方法比较[J].南京理工大学学报(自然科学版),2009,(01):122.
 LIN Fen,ZHAO You-qun.Comparison of Methods for Estimating Vehicle Side Slip Angle[J].Journal of Nanjing University of Science and Technology,2009,(05):122.
[5]李成国,牟善祥,张忠传,等.基于LTCC的Ka波段无源等效腔体分析与优化设计[J].南京理工大学学报(自然科学版),2009,(03):371.
 LI Cheng-guo,MU Shan-xiang,ZHANG Zhong-chuan.Analysis and Optimal Design of Passive Equivalent Cavity in Ka Wave Band Based on LTCC[J].Journal of Nanjing University of Science and Technology,2009,(05):371.
[6]钱晓东,王正欧.ART2神经网络聚类的改进研究[J].南京理工大学学报(自然科学版),2007,(01):71.
 QIAN Xiao-dong,WANG Zhen-ou.Improvement of Clustering of ART2 Neural Network[J].Journal of Nanjing University of Science and Technology,2007,(05):71.
[7]李千目,戚湧,张宏,等.IIDS的行为特征提取方法研究[J].南京理工大学学报(自然科学版),2004,(02):140.
 LI Qian-mu,QI Yong,ZHANG Hong,et al.Research on Method for Obtaining Action Character Based on IIDS[J].Journal of Nanjing University of Science and Technology,2004,(05):140.
[8]王树亮,王 东,冯 珍,等.基于小波包-神经网络故障诊断系统研究[J].南京理工大学学报(自然科学版),2004,(04):356.
 WANG Shu liang,WANG Dong,FENG Zhen,et al.Study of Fault Diagnosis System Based on Wavelet Packet-neural Network[J].Journal of Nanjing University of Science and Technology,2004,(05):356.
[9]徐晋.基于神经网络专家系统的创业企业信用等级评估研究[J].南京理工大学学报(自然科学版),2004,(06):684.
 XU Jin.Evaluation Index System of the Venture Enterprise’s Credit Level Based on Artificial Neural Network[J].Journal of Nanjing University of Science and Technology,2004,(05):684.
[10]孙怀江,余陈纲,杨静宇.ALVINN及其扩展[J].南京理工大学学报(自然科学版),2002,(02):175.
 SunHuaijiang YuChengang YangJingyu.ALVINN and Its Extensions[J].Journal of Nanjing University of Science and Technology,2002,(05):175.

备注/Memo

备注/Memo:
基金项目: 国家自然科学基金( 60374066) 作者简介: 秦华旺( 1978- ), 男, 江苏灌云人, 讲师, 博士生, 主要研究方向: 网络安全、入侵容忍, E_mail: q in_h_w @ 163. com。
更新日期/Last Update: 2012-12-19