【作者】 孟德；

【导师】 宋俊德；

【作者基本信息】 北京邮电大学 ， 计算机技术（专业学位）， 2013， 硕士

【摘要】 随着信息技术的不断进步和通信资费的不断下降,手机在人们的生活中变得越来越不可或缺。而在这光鲜的外表之下,手机病毒也随之悄悄走进了人们的生活。在计算机病毒日新月异的今天,手机病毒领域也没有停滞不前,出现了混合式感染的方式。手机使用者在取得新手机和使用手机安装新应用程序时都会有非常大的安全隐患。本课题针对这一问题,对基于聚类的手机病毒挖掘技术展开研究,力图实现一种聚类算法,提高手机病毒挖掘效率,降低算法时间复杂度,保持病毒挖掘准确性。本文选题自某大型外企的手机病毒挖掘引擎项目,主要完成项目中聚类挖掘模块的开发测试工作。本文首先讲述了手机病毒的基本概念和常见种类。分析了各类病毒的情况和发作机制。以及手机病毒的危害。并介绍了目前比较常见的几种病毒防治技术。之后介绍了数据挖掘的基础知识和常用聚类挖掘算法。对聚类挖掘技术进行了深入探究,说明了聚类算法通常使用的存储结构。并从逻辑、性能等方面对常见的K-means算法和DBSCAN算法进行了比较研究。为下一步的研究和实现工作进行了充分的理论和技术储备。同时也确定了本文选用基于K-means的增量算法处理手机病毒增量挖掘问题。本文在总结前人经验的基础上,结合手机病毒挖掘这一特定应用需求,对于K-means算法进行了改进和提升,通过对数据进行归一化处理使手机病毒挖掘准确率平均提升了15个百分点。同时提出了基于K-means算法的增量算法,可以对K-means挖掘后的数据进行有效的增量更新。并对算法内存使用等多方面进行了相应优化。同等条件下内存占用减少了50%左右,同时不改变挖掘结果。最后通过总结实验结果,提出了算法适宜应用的场景和聚类质量影响因素,为后续算法使用提供了良好的指导意见。更多还原

【Abstract】 With the continuous advancement of information technology. Mobile phones become increasingly indispensable in people’s lives. At the same time, the mobile phone virus along quietly into the people’s lives. Today, the computer virus is ever-changing. The field of mobile phone virus is not standing still. The field of mobile phone viruses appear the way Hybrid infection. When users use the phone to install a new application, there will be a very big security risk. To solve this problem, it is necessary to develop a simple and efficient mining engine on the mobile phone viruses excavation. The topic from a large foreign cooperation projects. The main task is the development and testing of clustering mining module.This paper first describes the basic concepts and common types of mobile phone virus. Analysis of the various types of the virus and attack mechanisms.Describe the dangers of mobile phone virus. And several relatively common virus prevention technology.Introduced to the basics of data mining and commonly used clustering mining algorithms. Delve into clustering mining technology. Illustrate the clustering algorithms typically use the storage structure. Carried out a comparative study of common K-means algorithm and DBSCAN algorithm. As the full theoretical and technical reserves for the next step in the research. This work determines the incremental algorithm based on K-means to deal with mobile phone virus incremental mining problems.This paper summarizes the experience of their predecessors. Taking into account the specific application requirements of the mobile phone virus mining. Improve and enhance the K-means algorithm. Through the application of a normalized data, phone virus mining accuracy increased by15%on average. Designed to achieve incremental algorithm based on K-means algorithm. Can effectively incremental mining the results of K means mining. Corresponding optimization algorithm memory usage, and many other. A50%reduction in memory footprint under the same conditions. Finally, the experimental results are summarized. Summarizes the algorithm suitable for the application scenario. Summarizes the clustering quality influencing factors. Provide a good guidance for future algorithm uses.更多还原