【作者】 马楠；

【导师】 邹仕洪；

【作者基本信息】 北京邮电大学 ， 计算机科学与技术， 2014， 硕士

【摘要】 随着移动电话的不断普及,短信业务发展迅猛,但它却是一把双刃剑,给生活带来便捷的同时,垃圾短信泛滥问题也一直困扰着人们,甚至带来一定的社会危害。为了还给用户一个绿色清洁的短信使用环境,进行垃圾短信的治理和过滤势在必行。本文主要研究了基于内容的垃圾短信过滤技术,设计了基于决策树和基于支持向量机的两种过滤系统。所做的主要工作包括前期调研相关背景技术知识；收集7155条短信(包含正常短信3600条,垃圾短信3555条),构建短信语料库；设计实现基于决策树和支持向量机的垃圾短信过滤系统,并进行对比实验。在基于决策树的垃圾短信过滤系统中,秉承业界广泛认可的“宁可放过、不可错杀”的垃圾短信过滤原则,提出采用传统短信特征与正常短信关键词新特征相结合,并将非常用字符所占短信比例新特征替代传统特征中的高危标点特征,以降低其带来误报率升高的风险。据此,本文设计实现了该系统,并进行了五组对比实验验证。在基于支持向量机的垃圾短信过滤系统中,创新性地提出采用二阶词特征,以提高单纯使用一阶词特征的垃圾短信过滤系统的准确率。据此,本文设计实现了该系统,并进行了九组对比实验验证。实验结果表明,本文提出的所有新特征均能够提升现有垃圾短信过滤系统的性能。更多还原

【Abstract】 With mobile phones abundantly available, short message service, for users, is a double-edged sword. It not only brings the convenience to our daily life, but also plagues the life with spam messages and even impacts on the stability and solidarity of the entire society. Therefore, to govern and identify spam messages is quite urgent, to make sure that users of mobile phones are in the clean environment of short messages.This paper mainly studies identifying methods of spam short messages based on content, so it designs two kinds of spam identifying system, including based on decision tree and based on support vector machine. The main work of the paper included the introduction of technical background knowledge, SMS corpus collection to build sample library and analysis, designing and implementing the spam SMS identifying systems based on decision tree and support vector machine, and the analysis of comparative experiments etc.In the research of spam identifying system based on decision tree, in order to meet the identifying system’s demand that is "would rather let the spam go, can’t victimize the normal", so this paper not only combined the traditional SMS features with the new feature of normal SMS keyword, but also replaced the feature of dangerous punctuation with the rate of unusual characteristic, to reduce the risk of high false positives. Accordingly, this paper designed and implemented this identifying system based on decision tree, and conducted experiments with five comparative groups.In the research of spam identifying system based on support vector machine, this paper innovatively put forward the feature combination of first-order word features and second-order word features, in order to improve the accuracy of the identifying system only by the simple first-order words as features. Accordingly, this paper designed and implemented this identifying system based on support vector machine, and conducted experiments with nine comparative groups.The results of these experiments showed that all of the new features put forward by this paper were able to improve the performance of existing spam identifying system.更多还原