垃圾邮件过滤系统的评估模型研究

在分析现有垃圾邮件过滤系统评估存在各指标值不一致的情况的基础上,提出一个综合评估垃圾邮件过滤系统过滤效果的评估模型.依据该评估模型,设计和实现了一个评估系统;并使用该评估系统评估了多个开源的垃圾邮件过滤系统.实验结果表明,提出的评估模型能够有效的综合评估垃圾邮件过滤系统的过滤效果.     

一种基于SMO算法的垃圾邮件过滤系统设计

垃圾邮件问题日益严重,给人们带来了极大困扰.基于SMO算法的垃圾邮件过滤方法将统计方法应用到垃圾邮件的判定上,是进行垃圾邮件处理的有效手段.本文介绍了基于SMO算法的垃圾邮件过滤系统模型,并对中文分词、特征选择、SMO算法等关键技术进行了阐述.SMO算法的引入势必会使系统在高效过滤垃圾邮件的同时,提高处理数据的速度.     

基于多级属性集的垃圾邮件过滤技术

针对目前常用的垃圾邮件过滤技术普遍存在误报和漏报与分类过滤效率之间的矛盾问题,提出了一种改进的垃圾邮件过滤算法。在对这种新算法进行仿真测试后发现,新算法不仅有效地降低了漏报率和误报率,同时也减少了分类时间,为改进现有垃圾邮件过滤系统提供了一条新的解决方法和途径。     

基于改进贝叶斯原理的垃圾邮件过滤算法研究

为了提高垃圾邮件过滤系统的对邮件过滤的准确性和返回率,论文改进了传统的贝叶斯定理。提出一种改进的垃圾邮件过滤方法,该方法使用基于单词提取特征值和使用特征向量来描述频率。模型降低了垃圾邮件的错误率,总体上提高了系统的过滤性能。与传统贝叶斯公式的假设不同,系统为垃圾邮件样本的每个特征值分配不同的权值,降低了的垃圾邮件判断误差。实验结果表明,论文提出的垃圾邮件过滤方法能够显着提高准确性和返回率,系统性能得到了较大改进。     

基于IDSS的中文垃圾邮件过滤模型设计

以智能决策支持系统结构为基础,提出了一种新的电子邮件过滤模型,并对中文垃圾邮件过滤中的中文分词及垃圾邮件特征知识库的更新等关键问题进行了探讨。开发了“智能邮件过滤系统(IEFS)”,使垃圾邮件误判率得到了一定程度的控制,有效防止了垃圾邮件的泛滥。     

基于最大熵方法的垃圾邮件过滤插件的设计与实现*

结合邮件的半结构化特征,将最大熵模型引入垃圾邮件过滤中,构造出基于最大熵模型的垃圾邮件过滤系统框架.在此基础上,将其与Outlook提供的PIA相结合,利用.NET技术开发出基于最大熵模型的垃圾邮件过滤插件,在客户端实现了基于内容的垃圾邮件过滤,较好地解决了垃圾邮件的问题.     

扩展内容过滤应对新型垃圾邮件

随着信息的迅猛增长,垃圾邮件问题日益严重。如何有效地过滤垃圾邮件成为研究的热点问题。介绍了目前比较常见的几种垃圾邮件过滤技术,分析了垃圾邮件制造者采用的各种新型手段,如简繁体混编、汉字拆分、词间加入特殊字符等,试图绕过基于内容的关键词检查。针对其中几种典型的新型垃圾邮件编写手段,提出改进的中文分词策略,结合基于内容的关键词检查,提出基于特征词扩展的内容检查过滤机制。实验验证改进后的过滤模型可在一定程度上提高对新型垃圾邮件的识别率。最后,对基于特征词扩展思想在网络内容安全和健康过滤上的应用做了展望。     

一种基于个性化邮件特征的反垃圾邮件系统

垃圾邮件的内容因人而异,现有的垃圾邮件过滤系统大多采用统一的过滤标准对用户的邮件进行过滤,因而忽略了垃圾邮件的这种个性化特征.针对这一情况提出一种个性化垃圾邮件过滤的计算模型,它事先不需要对模型进行针对性的训练,从对用户日常处理不同类型邮件的行为中分析和挖掘垃圾邮件的个性化特征,然后利用这种个性化特征在对垃圾邮件进行识别的同时不断强化这种个性化特征,以实现逐步提升对垃圾邮件识别率的目的.据此实现了相应的原型系统,通过对此系统的实验验证,该方法在现实环境下对垃圾邮件具有很好的过滤效果.     

智能决策支持的E-mail过滤模型

垃圾邮件的日益泛滥,严重制约了E-mail的应用,尤其是在大规模客服中心及重要商业核心机构中的应用.通过对智能决策支持系统结构的分析,提出了一种新的智能决策支持的E-mail过滤模型.采用一种规则与统计相结合的分词方法来完成中文分词,开发了智能邮件过滤系统(IEFS).由于用户决策的引入,以及垃圾邮件特征知识库的不断更新,使垃圾邮件误判率得到了一定程度的控制.     

基于IDSS的中文垃圾邮件过滤模型设计

以智能决策支持系统结构为基础，提出了一种新的电子邮件过滤模型．并对中文垃圾邮件过滤中的中文分词及垃圾邮件特征知识库的更新等关键问题进行了探讨。开发了“智能邮件过滤系统（JEFS）”，使垃圾邮件误判率得到了一定程度的控制．有效防止了垃圾邮件的泛滥。     

支持向量机在反垃圾邮件过滤中的应用

垃圾邮件对计算机系统的安全和人们的生活造成了严重的威胁,反垃圾邮件问题已经成为的具有重要现实意义的研究课题.针对垃圾邮件过滤本质是分类问题,提出了一种基于服务器前端的反垃圾邮件过滤方法,它采用了改进的v支持向量机算法对邮件内容进行分类,过滤垃圾邮件.研究结果表明该方法与直接的支持向量机增量算法相比,提高了过滤的准确率,具有一定的应用价值.     

A case-based technique for tracking concept drift in spam filtering

Spam filtering is a particularly challenging machine learning task as the data distribution and concept being learned changes over time. It exhibits a particularly awkward form of concept drift as the change is driven by spammers wishing to circumvent spam filters. In this paper we show that lazy learning techniques are appropriate for such dynamically changing contexts. We present a case-based system for spam filtering that can learn dynamically. We evaluate its performance as the case-base is updated with new cases. We also explore the benefit of periodically redoing the feature selection process to bring new features into play. Our evaluation shows that these two levels of model update are effective in tracking concept drift.     

Automatic thesaurus construction for spam filtering using revised back propagation neural network

Email has become one of the fastest and most economical forms of communication. Email is also one of the most ubiquitous and pervasive applications used on a daily basis by millions of people worldwide. However, the increase in email users has resulted in a dramatic increase in spam emails during the past few years. This paper proposes a new spam filtering system using revised back propagation (RBP) neural network and automatic thesaurus construction. The conventional back propagation (BP) neural network has slow learning speed and is prone to trap into a local minimum, so it will lead to poor performance and efficiency. The authors present in this paper the RBP neural network to overcome the limitations of the conventional BP neural network. A well constructed thesaurus has been recognized as a valuable tool in the effective operation of text classification, it can also overcome the problems in keyword-based spam filters which ignore the relationship between words. The authors conduct the experiments on Ling-Spam corpus. Experimental results show that the proposed spam filtering system is able to achieve higher performance, especially for the combination of RBP neural network and automatic thesaurus construction.     

On the properties of spam-advertised URL addresses

The main purpose of most spam e-mail messages distributed on Internet today is to entice recipients into visiting World Wide Web pages that are advertised through spam. In essence, e-mail spamming is a campaign that advertises URL addresses at a massive scale and at minimum cost for the advertisers and those advertised. Nevertheless, the characteristics of URL addresses and of web sites advertised through spam have not been studied extensively. In this paper, we investigate the properties of URL-dissemination through spam e-mail, and the characteristics of URL addresses disseminated through spam. We conclude that spammers advertise URL addresses non-repetitively and that spam-advertised URLs are short-lived, elusive, and therefore hard to detect and filter. We also observe that reputable URL addresses are sometimes used as decoys against e-mail users and spam filters. These observations can be valuable for the configuration of spam filters and in order to drive the development of new techniques to fight spam.     

Personalized email management at network edges

A new technique for managing and disseminating Web-based email prefetches messages and generates dynamic pages, displaying them at the network edge. Compared to other popular Web-based email servers, the prefetching and caching emails (PACE) prototype shows an improved performance with respect to user-perceived latency. Additionally, PACE'S centralized neural-network-based personalized spam filter will filter spam and viruses at the server's origin, thus saving bandwidth. Another major concern for users is the email accounts being clogged with spam. Spam filters can be classified as server-side or client-side. Server-side filters are integrated with email servers and filter out spam at the server end.     

基于网络会话层的垃圾邮件行为识别

目前最流行的邮件内容过滤技术工作在网络应用层,通过对邮件内容的分析来判别邮件的合法性,无法避免由于垃圾邮件的泛滥而造成的网络带宽资源的浪费。针对这种情况,论文提出一种基于网络会话层的垃圾邮件行为识别方法。该方法运用决策树算法,对邮件发送过程中的网络会话层数据进行挖掘,发现垃圾邮件的行为规律,在垃圾邮件的内容数据发送前就对其实施过滤,有效地解决了垃圾邮件占用网络带宽的问题,是对当前各种垃圾邮件过滤技术的一个有益的补充。     

改进ReliefF算法在图像型垃圾邮件检测中的应用研究*

图像型垃圾邮件的传播给社会和人民生活造成了极大的负面影响。一些垃圾图像过滤技术的应用在一定程度上遏制了它的泛滥,但是在时间消耗和精确度方面很难兼顾。在对垃圾邮件图像的特征数据深入分析后,提出一种基于特征冗余度的ReliefF特征选择算法(R-ReliefF算法)。本算法首先获取图像特征,结合数据特征进行离散化,并对这些离散化后的特征集合进行优化,最后应用在垃圾图像识别上。对比发现,优化后提取的特征子集在识别垃圾邮件图像方面既减少了时间消耗,又提高了垃圾图像识别的精确度。