云环境下基于SOM神经网络的入侵检测方法研究
Research on Intrusion Detection Method Based on SOM Neural Network in Cloud Environment

作者: 赵津 :华北电力大学控制与计算机工程学院,河北 保定; 朱有产 :华北电力大学信息与网络管理中心,河北 保定;

关键词: 入侵检测SOM神经网络微粒群算法模拟退火算法Intrusion Detection SOM Neural Network Particle Swarm Optimization Algorithm Simulated Annealing Algorithm

摘要: 云安全已成为云计算发展过程中面临的重要挑战,基于云计算的入侵检测系统将成为云安全体系的重要组成部分。根据云计算特点和安全需求,设计了一种适合云环境的入侵检测系统模型,在入侵检测算法中引入SOM自组织特征映射神经网络算法,对SOM网络连接权值随机初始化可能导致的训练失败问题,采用基于模拟退火的微粒群算法对其进行优化,通过仿真实验验证优化算法可有效提高入侵检测性能。

Abstract: Cloud security has become an important challenge in the development of cloud computing. The intrusion detection system based on cloud computing will be an important part of the cloud security system. According to the characteristics and security requirements of cloud computing, an intrusion detection system model is designed for the cloud environment, and the SOM self-organiz- ing feature map neural network algorithm is introduced into the intrusion detection algorithm. The random initialization of SOM network connection weights may lead to the failure of the training, so the particle swarm optimization algorithm based on simulated annealing is used to optimize the SOM neural network algorithm. The simulation experiment results show that the optimization algorithm can effectively improve the performance of intrusion detection.

文章引用: 赵津 , 朱有产 (2016) 云环境下基于SOM神经网络的入侵检测方法研究。 计算机科学与应用, 6, 505-513. doi: 10.12677/CSA.2016.68063

参考文献

[1] 冯登国, 张敏, 张妍, 徐震. 云计算安全研究[J]. 软件学报, 2011, 22(1): 71-83.

[2] 刘伉伉. 云计算环境下入侵检测技术的研究[D]: [硕士学位论文]. 济南: 山东师范大学, 2015.

[3] 刘鹏. 云计算[M]. 第二版. 北京: 电子工业出版社, 2011.

[4] Mazzariello, C., Bifulco, R. and Canonico, R. (2010) Integrating a Network IDS into an Open Source Cloud Computing Environment. 2010 Sixth International Conference on Information Assurance and Security (IAS), Atlanta, 23-25 August 2010, 265-270.

[5] 谭秀辉. 自组织神经网络在信息处理中的应用研究[D]: [博士学位论文]. 太原: 中北大学, 2015.

[6] 王芳. 粒子群模拟退火融合算法及其在物流配送问题中的应用[D]: [硕士学位论文]. 上海: 华东理工大学, 2010.

[7] Shi, Y. and Eberhart, R.C. (1998) A Modified Particle Swarm Optimizer. The 1998 IEEE International Conference on Evolutionary Computation Proceedings, Anchorage, 4-9 May 1998, 69-73.
http://dx.doi.org/10.1109/icec.1998.699146

[8] Shi, Y. and Eberhart, R.C. (1999) Empirical Study of Particle Swarm Optimization. Proceedings of the 1999 Congress on Evolutionary Computation, Washington DC, 6-9 July 1999, 1945-1950.
http://dx.doi.org/10.1109/CEC.1999.785511

[9] 吴剑, 冯国瑞. 基于模拟退火和半监督聚类的入侵检测方法[J]. 计算机与现代化, 2014(11): 27-30.

[10] 涂晓芝, 颜学峰, 钱锋. PSO-SOM分类判别研究及其应用[J]. 高技术通讯, 2006, 16(10): 1014-1018.

[11] KDD Cup 1999 Data Set (1999). http://archive.ics.uci.edu/ml/databases/kddcup99/kddcup99.html

[12] 飞思科技产品研发中心. MATLAB6.5辅助神经网络分析与设计[M]. 北京: 电子工业出版社, 2003.

[13] 侯梅菊. 计算智能技术在入侵检测系统中的应用研究[D]: [硕士学位论文]. 重庆: 重庆大学, 2012.

分享
Top