云环境下移动智能终端入侵检测方法研究

发布时间：2021-01-28 11:59

　　随着互联网技术的迅猛发展,以及云计算与移动智能终端技术的相互融合产生了移动云计算（Mobile Cloud Computing,MCC）,并引起学术界和工业界的广泛关注。根据2016年思科IBSG数据显示,全球近85%的人口都在使用移动终端设备。然而,由于MCC具有分布式、用户访问量大和操作简便等特性,入侵者亦可在无管理员授权的情况下使用云计算和云存储等服务。针对移动云计算中的安全问题,许多研究学者采用防火墙技术和入侵检测系统（Intrusion Detection System,IDS）等网络信息安全技术来保障移动云计算安全。但现有研究依然存在较多的安全问题,如防火墙技术可扩展性和自适应性较差,IDS的检测精度低、误报率高以及数据属性冗余等问题。针对现有IDS存在的上述问题,论文采用基于分类和信息论的机器学习方法构建了入侵检测系统的入侵检测模型。论文采用的基于分类的机器学习方法和特征选择方法包括:支持向量机（Support Vector Machine,SVM）,随机森林（Random Forest,RF）,信息增益（Information Gain,IG）和用于进化特征选择的Map... 

【文章来源】：兰州理工大学甘肃省

【文章页数】：97 页

【学位级别】：硕士

【文章目录】：
中文摘要
Abstract
1.Introduction
    1.1 Research Background and Significance
    1.2 Research Status for Related Works
        1.2.1 Intrusion detection system（IDS）
        1.2.2 Feature selection method
        1.2.3 MapReduce for evolutionary feature selection（MR-EFS）
        1.2.4 Dataset description
    1.3 Research Innovations and Main Objectives
    1.4 Organization Structure and Arrangement
2.Related Concepts and Basic Principles
    2.1 Mobile Intelligent Terminal based Cloud Computing
        2.1.1 Overview of cloud computing
        2.1.2 Concept of mobile cloud computing（MCC）
    2.2 Intrusion Detection System
        2.2.1 Basic concept of intrusion detection system
        2.2.2 Intrusion detection framework
        2.2.3 Intrusion detection performance metrics
    2.3 Feature Selection Method
        2.3.1 Information gain（IG）based feature selection
        2.3.2 MapReduce for evolutionary feature selection（MR-EFS）
    2.4 Support Vector Machine（SVM）
    2.5 Random Forest（RF）Classifier
    2.6 Brief Summary
3.Intrusion Detection Method Based on SVM and Information Gain for Mobile Cloud Computing
    3.1 Introduction
    3.2 Intrusion Detection Method based on SVM and IG for MCC
    3.3 Experimental Results and Analysis
        3.3.1 Dataset and data preprocessing
        3.3.2 Information gain based feature selection method
        3.3.3 Performance metrics
        3.3.4 Performance evaluation
        3.3.5 Experimental result and discussion
    3.4 Brief Summary
4.Intrusion Detection Method for MCC based on MapReduce for Evolutionary Feature Selection
    4.1 Introduction
    4.2 Implementation of Random Forest Classifier Integrated with IDS Model
    4.3 Experimental Results and Analysis
        4.3.1 Dataset collection and data preprocessing
        4.3.2 MapReduce for evolutionary feature selection
        4.3.3 Performance metrics
        4.3.4 Experimental result and discussion
    4.4 Brief Summary
Conclusions and Future work
References
Acknowledgement
Appendix A.Academic papers published during the master's degree program
Appendix B.Key Codes used in this thesis


【参考文献】：
期刊论文
[1]融合FAST特征选择与ABQGSA-SVM的网络入侵检测[J]. 李丛,闫仁武,朱长水,高广银.  计算机应用研究. 2017(07)
[2]Intrusion Detection Algorithm Based on Density,Cluster Centers,and Nearest Neighbors[J]. Xiujuan Wang,Chenxi Zhang,Kangfeng Zheng.  中国通信. 2016(07)
[3]基于云计算的移动智能终端入侵检测方法研究[J]. 李慧芳,彭新光.  计算机仿真. 2016(03)
[4]基于PCA的SVM网络入侵检测研究[J]. 戚名钰,刘铭,傅彦铭.  信息网络安全. 2015(02)
[5]云模型半监督聚类动态加权的入侵检测方法[J]. 张杰,李永忠.  昆明理工大学学报(自然科学版). 2013(04)

硕士论文
[1]基于云端的移动智能终端入侵检测机制研究[D]. 赵雪.辽宁大学 2015



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