[1]牛永洁,薛宁静. 改进的免疫克隆算法在入侵检测中的应用[J].计算机技术与发展,2016,26(05):86-90.
　NIU Yong-jie,XUE Ning-jing. Application of Improved Immune Clonal Selection Algorithm in Intrusion Detection[J].,2016,26(05):86-90.
点击复制

 改进的免疫克隆算法在入侵检测中的应用()
分享到：

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

卷:
26
期数:
2016年05期
页码:
86-90
栏目:
安全与防范
出版日期:
2016-05-10

文章信息/Info

Title:
 Application of Improved Immune Clonal Selection Algorithm in Intrusion Detection
文章编号:
1673-629X（2016)05-0086-05
作者:
 牛永洁; 薛宁静
 延安大学 数学与计算机学院
Author(s):
 NIU Yong-jie; XUE Ning-jing
关键词:
 ; 入侵检测; 克隆选择; 变异概率; 克隆策略; 自适应
Keywords:
 ; intrusion detection; clonal selection; mutation probability; cloning strategy; adaptive
分类号:
TP301.6
文献标志码:
A
摘要:
 为了提高入侵检测系统的正确率,降低误检率,对基本的免疫克隆选择算法采用抗体的克隆数目与亲和度成正比且克隆数目线性递减、变异概率线性递减、新的替换策略、变异概率和抗体克隆数量突变进行改进.对抗体克隆策略的改进保证了算法的收敛速度,避免了算法后期的震荡,变异概率的自适应变化加强了算法后期的收敛,新的替换策略、变异概率和抗体克隆数量突变能够有效地避免算法陷入局部最优.经过KDD Cup 1999数据集的训练和检验数据的仿真测试,改进后的算法具有较高的检测正确率和较低的误检率,而且新算法收敛速度快,不易"早熟".
Abstract:
 In order to improve the correct rate of intrusion detection system and reduce false positive rate,on the basic immune clonal se-lection algorithm using antibody clone number and affinity is proportional to the degree and the number of clones linear decreasing,muta-tion probability decreasing linearly,new replacement strategy,the number of mutation probability and antibody clonal mutation for im-provement. The improvement of antibody cloning strategy to ensure the convergence speed of the algorithm and avoid the shock of the late. Adaptive changes in the mutation probability strengthen the convergence of the algorithm for the late. New replacement strategy and the number of mutation probability and antibody clonal mutations can effectively avoid the algorithm into a local optimum. After the train-ing and testing for Cup KDD 1999 data set,the improved algorithm has the advantages of higher detection accuracy rate and lower false detection rate,with fast convergence speed,and it is not easy to "premature".

相似文献/References:

[1]李雷 丁亚丽 罗红旗.基于规则约束制导的入侵检测研究[J].计算机技术与发展,2010,(03):143.
　LI Lei,DING Ya-li,LUO Hong-qi.Intrusion Detection Technology Research Based on Homing - Constraint Rule[J].,2010,(05):143.
[2]马志远,曹宝香.改进的决策树算法在入侵检测中的应用[J].计算机技术与发展,2014,24(01):151.
　MA Zhi-yuan,CAO Bao-xiang.Application of Improved Decision Tree Algorithm in Intrusion Detection System[J].,2014,24(05):151.
[3]高峥 陈蜀宇 李国勇.混合入侵检测系统的研究[J].计算机技术与发展,2010,(06):148.
　GAO Zheng,CHEN Shu-yu,LI Guo-yong.Research of a Hybrid Intrusion Detection System[J].,2010,(05):148.
[4]林英 张雁 欧阳佳.日志检测技术在计算机取证中的应用[J].计算机技术与发展,2010,(06):254.
　LIN Ying,ZHANG Yan,OU Yang-jia.Application of Log Testing Technology in Computer Forensics[J].,2010,(05):254.
[5]李钦 余谅.基于免疫遗传算法的网格入侵检测模型[J].计算机技术与发展,2009,(05):162.
　LI Qin,YU Liang.Grid Intrusion Detection Model Based on Immune Genetic Algorithm[J].,2009,(05):162.
[6]黄世权.网络存储安全分析[J].计算机技术与发展,2009,(05):170.
　HUANG Shi-quan.Analysis of Network Storage＇s Safety[J].,2009,(05):170.
[7]李睿 肖维民.基于孤立点挖掘的异常检测研究[J].计算机技术与发展,2009,(06):168.
　LI Rui,XIAO Wei-min.Research on Anomaly Intrusion Detection Based on Outlier Mining[J].,2009,(05):168.
[8]胡琼凯 黄建华.基于协议分析和决策树的入侵检测研究[J].计算机技术与发展,2009,(06):179.
　HU Oiong-kai,HUANG Jian-hua.Intrusion Detection Based on Protocol Analysis and Decision Tree[J].,2009,(05):179.
[9]汪世义.基于优化支持向量机的网络入侵检测技术研究[J].计算机技术与发展,2009,(07):177.
　WANG Shi-yi.Network Intrusion Detection Based on Improved Support Vector Machine[J].,2009,(05):177.
[10]薛俊 陈行 陶军.一种基于神经网络的入侵检测技术[J].计算机技术与发展,2009,(08):148.
　XUE Jun,CHEN Hang,TAO Jun.Technology of Intrusion Detection Based on Neural Network[J].,2009,(05):148.
[11]李蓉,周维柏. 基于多特征选取和类完全加权的入侵检测[J].计算机技术与发展,2014,24(07):145.
　LI Rong,ZHOU Wei-bai. Intrusion Detection Based on Multiple Feature Selection and Class Fully Weighted [J].,2014,24(05):145.
[12]李锋. 粒子群模糊聚类算法在入侵检测中的研究[J].计算机技术与发展,2014,24(12):138.
　LI Feng. Research on Fuzzy Clustering Algorithm Based on PSO in IDS[J].,2014,24(05):138.
[13]朱俚治. 一种基于误用检测的新算法[J].计算机技术与发展,2015,25(02):135.
　ZHU Li-zhi. A New Algorithm Based on Misuse Detection[J].,2015,25(05):135.
[14]陈桂林,王生光,徐静妹,等. 基于GA和组合核的SVM入侵检测算法[J].计算机技术与发展,2015,25(02):148.
　CHEN Gui-lin,WANG Sheng-guang,XU Jing-mei,et al. Intrusion Detection Algorithm of SVM Based on GA and Composed Kernel Function[J].,2015,25(05):148.
[15]张公让,万飞. 基于网格搜索的 SVM 在入侵检测中的应用[J].计算机技术与发展,2016,26(01):97.
　ZHANG Gong-rang,WAN Fei. Application of Support Vector Machine in Network Intrusion Detection Based on Grid Search[J].,2016,26(05):97.
[16]刘华春,候向宁,杨忠. 基于改进K均值算法的入侵检测系统设计[J].计算机技术与发展,2016,26(01):101.
　LIU Hua-chun,HOU Xiang-ning,YANG Zhong. Design of Intrusion Detection System Based on Improved K-means Algorithm[J].,2016,26(05):101.
[17]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.
　HE Wen-he[],LI Tao-shen[][],HUANG Ru-wei[][]. Intrusion Detection Model Based on Improved BP Algorithm in Cloud Environment[J].,2016,26(05):87.
[18]陈天宇,吴凡,马世杰,等. 基于CS和LS-SVM的入侵检测算法[J].计算机技术与发展,2016,26(05):99.
　CHEN Tian-yu,WU Fan,MA Shi-jie,et al. Intrusion Detection Algorithm Based on Compressed Sensing and Least Square Support Vector Machine[J].,2016,26(05):99.
[19]胡波. 基于簇的移动自组网的IDMEF数据模型设计[J].计算机技术与发展,2016,26(08):93.
　HU Bo. Design of Data Model Intrusion of Detection Message Exchange Format in Wireless Ad Hoc Networks Based on Clusters[J].,2016,26(05):93.
[20]曹耀彬,王亚刚. 免疫算法优化的RBF在入侵检测中的应用[J].计算机技术与发展,2017,27(06):114.
　CAO Yao-bin,WANG Ya-gang. Application of RBF Neural Network Optimized by Immune Algorithm in Intrusion Detection[J].,2017,27(05):114.

更新日期/Last Update: 2016-09-19