菜单
  

    摘要随着互联网的发展,人们在享用 Internet 带来的便利的同时,亦饱受各种攻击 行为的烦扰,网络安全防护对各种计算机系统的重要性不言而喻。而除防火墙等 静态防御机制之外,人们已经意识到动态防御机制在网络已经遭到入侵后对安全 威胁分析的重要意义。入侵检测系统的研究由此应运而生。传统的 web 日志分析 或入侵检测系统基本使用的是安全专家分析提取的规则或模型,其准确率低、效 率不高、可扩展性差。而机器学习技术的优势就在于它能从大量数据中发现特征 和行为模式,将两者结合在一起,就可以有效地减少人工的工作量和经验成分, 使安全威胁分析具有自适应和自我学习的能力。68520

    本文通过研究入侵检测系统及其机器学习算法,将决策树分类算法应用于入 侵检测系统,利用云计算中的并行计算,来处理可能的海量数据,以提高整个系 统的检测性能,提高运行效率和准确率。本文的工作主要有以下几点:1、通过研 究和分析传统入侵检测技术的局限性和信息时代海量数据的特点,从而发现将机 器学习技术应用于入侵检测系统并进行并行化是十分必要的。2、深入分析了决策 树算法,详细介绍了 SLIQ 算法,并针对其用于入侵检测系统的缺陷提出了优化 方案,进行了实验验证。3、将用于入侵检测系统的 SLIQ 算法移植到 Hadoop 平 台上进行计算,提高其运行效率。

    毕业论文关键字:入侵检测 决策树 SLIQ Hadoop 并行化

    Title Security Threat Analysis of Web Application In Cloud Computing Environment

     

     

    Abstract

    With the development of the Internet, people are suffering from a variety of network attacks while enjoying the convenience brought by the Internet. The importance of network security to various computer systems goes without saying in nowadays. And in addition to static defense mechanisms such as firewalls, people have realized the significance of the dynamic defense mechanism to the security threat analysis in the network which has been compromised. Thus, the research of intrusion detection systems came into being. Traditional web log analysis or intrusion detection systems are basically using the rules or models which are extracted by security experts. So the rules or models are unavoidable to have low accuracy, low efficiency and poor scalability. However, the advantages of machine learning technology is that it can discover the characteristics and behavior patterns from large amounts of data. If we put the two together, we will effectively reduce the manual workload and experience ingredients in the models or rules, so that the security threat analysis will be more adaptive and have self-learning ability.

     

    In this paper, by researching in intrusion detection systems and machine learning algorithms, I apply the decision tree classification algorithm to intrusion systems. In order to deal with massive amounts of data, the use of parallel computing in the cloud improves the detection performance, efficiency and accuracy of the whole system. The main work of this paper

    are the following: firstly, by studying and analyzing the limitations of traditional intrusion detection technology and the characteristics of massive data in the information age, I found that it is absolutely essential for the machine learning techniques to use in intrusion detection systems and to be parallelized. Secondly, I have analyzed the decision tree algorithm in depth and described the SLIQ algorithm. And then I proposed optimization methods for its shortcomings in the appliance of IDS and verified them by experiments. Thirdly, the SLIQ algorithm which is used in IDS was ported to the Hadoop computing platform to improve its operating efficiency.

  1. 上一篇:基于RGB-D摄像机的图像分割算法研究与实现
  2. 下一篇:android冠字号码的二维码打印及查看软件设计
  1. 基于Hadoop的制造过程大数据存储平台构建

  2. 云虚拟环境下资源分配优化算法的研究

  3. 网络环境下电子报纸开发利用现状及模式研究

  4. C#+SQLserver农业环境信息管理系统设计与实现

  5. 云计算服务OpenStack的代数规约描述技术

  6. Linux环境下远程管理平台基础程序设计与实现

  7. Morphin算法面向非结构化道...

  8. 乳业同业并购式全产业链...

  9. 十二层带中心支撑钢结构...

  10. 杂拟谷盗体内共生菌沃尔...

  11. 电站锅炉暖风器设计任务书

  12. 中考体育项目与体育教学合理结合的研究

  13. 河岸冲刷和泥沙淤积的监测国内外研究现状

  14. 当代大学生慈善意识研究+文献综述

  15. 酸性水汽提装置总汽提塔设计+CAD图纸

  16. 大众媒体对公共政策制定的影响

  17. java+mysql车辆管理系统的设计+源代码

  

About

751论文网手机版...

主页:http://www.751com.cn

关闭返回