Spam Filtering With Dynamically Updated URL Statistics

Many URL-based spam filters rely on "white" and "black" lists to classify email. The authors' proposed URL-based spam filter instead analyzes URL statistics to dynamically calculate the probabilities of whether email with specific URLs are spam or legitimate, and then classifies them accordingly.     

邮件过滤算法的比较

探讨了邮件过滤器的体系,介绍了6种常用的过滤算法.在4个公用的数据集上,利用3个评价指标对这6种算法进行了全面的比较.实验的主要结论有:集成过滤器的方法有利于提高过滤效果;与其它两个数据集相比,PU1和Ling-spam数据集用于评价过滤器时会得出相对乐观的结果;NB和k-NN的表现不稳定,对数据集的敏感程度较高.对于邮件过滤研究者了解、改进已有算法,提出新的算法有一定的参考价值.     

基于P2P协作的垃圾邮件发送行为识别技术研究

在分析目前垃圾邮件过滤技术的基础上,并根据垃圾邮件大量发送行为特征,提出了一种基于P2P协作的垃圾邮件发送行为识别技术。该技术将各邮件服务器组成一个反垃圾邮件（Anti-Spam）P2P网络,每个邮件服务器储存可疑邮件信息并将这些信息共享在Anti-Spam P2P网络上,然后根据可疑邮件信息在Anti-Spam P2P网络上进行协作识别垃圾邮件。实验结果表明,该技术是针对垃圾邮件的群发特征而不依赖于邮件内容、语言类型或格式分析,在MTA阶段就能过滤大量垃圾邮件,提高了处理速度和准确率并节省大量的系统资源,具有良好的过滤性能。     

基于神经网络集成的垃圾邮件过滤系统设计

垃圾邮件的处理是电子邮件服务中非常重要的功能,该文在对标准邮件集表示为向量空间模型,降维处理处理工作的基础上,运用神经网络集成的方法来构造邮件分类器,对邮件进行过滤;该方法在垃圾邮件语料库上进行了实验,实验证明该方法对于垃圾邮件的过滤有较好的效果。     

垃圾邮件过滤技术的发展与现状

电子邮件随着Intemet的发展给人们带来了方便，随之而来的垃圾邮件却给人们带来无尽的烦恼。本文针对反垃圾邮件技术发展与现状，对目前已应用或正在研究的垃圾邮件过滤技术进行了分析，为项目组改进垃圾邮件过滤方法的下一步工作做前期准备。     

垃圾邮件过滤技术的发展与现状

电子邮件随着Internet的发展给人们带来了方便,随之而来的垃圾邮件却给人们带来无尽的烦恼.本文针对反垃圾邮件技术发展与现状,对目前已应用或正在研究的垃圾邮件过滤技术进行了分析,为项目组改进垃圾邮件过滤方法的下一步工作做前期准备.     

基于领域知网的垃圾邮件过滤方法

垃圾过滤技术的一个重要指标是误判率。把用户的一封正常邮件误判为垃圾邮件,其损失更大。领域知网是某领域内的知识库。提出为常见垃圾邮件类别建立领域知网,当遇到难判别的邮件时,使用基于领域知网的过滤技术,可以降低垃圾邮件的误判率。还介绍了构建领域知网的方法,以及其他关键技术。     

基于Logistic回归的中文垃圾邮件过滤方法

研究如何实现Logistic回归模型在中文垃圾邮件过滤中的应用,给出了关键技术,并将其应用于SEWM20071)垃圾邮件语料库上,取得了较优的过滤效果.还分析了影响正常邮件误判率、垃圾邮件误判率和精确率等因子.对比实验结果表明,应用于中文垃圾邮件过滤的Logistic回归模型与SVM相比具有更优的ROC指标和更快的运行效率.     

一种基于学习向量量化网络的垃圾邮件过滤方法

伴随着电子邮件的广泛使用,垃圾邮件泛滥成灾,严重影响了人们正常的学习、工作和生活。拳文针对目前的垃圾邮件主要是由多种商业或政治性类别的垃圾邮件组成的特点,利用学习向量量化网络能把多个子类合并成一个复杂大类的特性,构建了一个反垃圾邮件的LVQ神经网络模型,我们对该LVQ网络模型进行了与其他算法的对比试验,试验表明它比基于贝叶斯公式算法和基于神经网络BP算法的过滤器有更好的性能。     

An effective multi-layered defense framework against spam

Spam is a big problem for email users. The battle between spamming and anti-spamming technologies has been going on for many years. Though many advanced anti-spamming technologies are progressing significantly, spam is still able to bombard many email users. The problem worsens when some anti-spamming methods unintentionally filtered legitimate emails instead! In this paper, we first review existing anti-spam technologies, then propose a layered defense framework using a combination of anti-spamming methods. Under this framework, the server-level defense is targeted for common spam while the client-level defense further filters specific spam for individual users. This layered structure improves on filtering accuracy and yet reduces the number of false positives. A sub-system using our pre-challenge method is implemented as an add-on in Microsoft Outlook 2002. In addition, we extend our client-based pre-challenge method to a domain-based solution thus further reducing the individual email users' overheads.     

Artificial immune system inspired behavior-based anti-spam filter

This paper proposes a novel behavior-based anti-spam technology for email service based on an artificial immune-inspired clustering algorithm. The suggested method is capable of continuously delivering the most relevant spam emails from the collection of all spam emails that are reported by the members of the network. Mail servers could implement the anti-spam technology by using the “black lists” that have been already recognized. Two main concepts are introduced, which defines the behavior-based characteristics of spam and to continuously identify the similar groups of spam when processing the spam streams. Experiment results using real-world datasets reveal that the proposed technology is reliable, efficient and scalable. Since no single technology can achieve one hundred percent spam detection with zero false positives, the proposed method may be used in conjunction with other filtering systems to minimize errors.     

基于Naive Bayesian算法的客户端邮件过滤器的实现

“垃圾”邮件是Internet上面临急待解决的问题.Naive Bayesian过滤器由于其简单高效性在文本分类中应用较广,重点研究了Naive Bayesian算法,给出了一个“垃圾”邮件过滤器,依据邮件的内容而不是通过设置规则来过滤邮件,并通过实验论证了它在客户端过滤邮件的可行性和有效性.     

MSRep:利用模糊控制的邮件服务器信誉度生成算法

在分析现有的信誉度生成算法的基础上,结合模糊控制技术设计了一个参数模糊控制器,提出了一种分布式网络中基于模糊控制的邮件服务器信誉度生成算法MSRep。MSRep算法使用参数模糊控制器控制其参数的取值,得到的信誉度更符合信誉度计算原则。仿真结果显示,与现有的信誉度生成算法相比,当邮件服务器开始恶意行为时,MSRep算法计算得到的信誉度下降更快;当邮件服务器重建自身的信誉度时,其信誉度上升则更加缓慢。在不同的发送模型中,MSRep算法计算得到的重建指数平均是TrustGuard算法的4.389倍,恶意节点为发送垃圾邮件所付出的代价更高。     

基于Bayes的一种改良垃圾邮件过滤模型

文章首先分析了垃圾邮件的产生机理,介绍了目前比较常见的几种垃圾邮件过滤技术,然后从朴素贝叶斯的理论依据出发,针对当前应用于重要商业领域的垃圾邮件过滤系统的不足,设计了一种应用多级邮件策略的新模型,并通过实验比较证明新模型的应用在一定程度上提高了垃圾邮件过滤系统的查全率和查准率。     

垃圾邮件绕发问题探析

反垃圾邮件网关的出现,使垃圾邮件的数量大大减少,然而再先进的垃圾邮件过滤技术也无法对绕过网关的邮件进行处理。本文对产生此问题的根源进行了深入的探析,并给出了解决问题的几种方法。     

基于电子邮件原理的垃圾邮件过滤策略

随着网络应用范围、领域的不断扩大,垃圾邮件也越来越多。为了防止垃圾邮件对网络和用户带来不必要的麻烦,从电子邮件系统的原理、结构出发,在电子邮件的发送、接收过程中研究如何对邮件的发送者进行身份认证。在电子邮件的发送服务器、接收服务器、接收者三个层次上分析垃圾邮件过滤方法。     

基于LDA模型的博客垃圾评论发现

Blog(博客)作为一种新兴的网络媒体,在很大程度上增强了互联网的开放性,Blog已经成为互联网上的主要信息源之一,这也使得Blog空间中的垃圾评论成倍增长,因此如何识别垃圾评论成为面临的重要问题。该文首先借鉴处理垃圾邮件的方法,针对Blog本身的特点,使用规则初步过滤垃圾评论,然后对剩余评论,利用Latent Dirichlet Allocation(LDA) 这种能够提取文本隐含主题的产生式模型,对博客中的博文进行主题提取,并结合主题信息进行判断,从而识别Blog空间的垃圾评论。通过实验验证,该方法可以发现大多数垃圾评论,实验取得了较好的结果,使Blog信息更加准确、有效的为用户使用。     

Online active multi-field learning for efficient email spam filtering

Email spam causes a serious waste of time and resources. This paper addresses the email spam filtering problem and proposes an online active multi-field learning approach, which is based on the following ideas: (1) Email spam filtering is an online application, which suggests an online learning idea; (2) Email document has a multi-field text structure, which suggests a multi-field learning idea; and (3) It is costly to obtain a label for a real-world email spam filter, which suggests an active learning idea. The online learner regards the email spam filtering as an incremental supervised binary streaming text classification. The multi-field learner combines multiple results predicted by field classifiers in a novel compound weight schema, and each field classifier calculates the arithmetical average of multiple conditional probabilities calculated from feature strings according to a data structure of string-frequency index. Comparing the current variance of field classifying results with the historical variance, the active learner evaluates the classifying confidence and takes the more uncertain email as the more informative sample for which to request a label. The experimental results show that the proposed approach can achieve the state-of-the-art performance with greatly reduced label requirements and very low space-time costs. The performance of our online active multi-field learning, the standard (1-ROCA)% measurement, even exceeds the full feedback performance of some advanced individual text classification algorithms.     

垃圾邮件行为识别技术研究

对反垃圾邮件行为识别技术进行了研究。提出了一种基于会话层的垃圾邮件识别方法,在分析发送过程中的邮件行为特征基础上,提取出能够区分垃圾邮件和正常邮件的行为特征,并采用支持向量机分类算法建立行为特征识别模型,找出垃圾邮件行为规律。该方法在邮件正文发送之前对垃圾邮件进行过滤,能够有效地节省带宽。采用真实的邮件数据集合分别使用行为识别技术与基于内容的过滤技术进行实验,验证该技术具有较好的邮件分类能力。     

基于多层架构的邮件过滤系统

介绍了一种工作在Linux环境下、基于多层架构邮件过滤系统的实现,通过在邮件传递的不同层次提供过滤,有效地节制垃圾邮件和病毒邮件的泛滥.