基于后缀树的骨干网络垃圾邮件检测方法

虽然目前垃圾邮件过滤或检测的研究比较多,但是它们大多数是基于邮件客户端。文章提出了一种基于后缀树的骨干网络垃圾邮件检测方法,它采用后缀树文本表示方法,通过不定长统计方法判定邮件是否相似,然后利用邮件重复出现的次数判定是否为垃圾邮件。该方法不需要任何训练,直接对接收的邮件进行分类统计;对于长度为的邮件,算法的时间复杂度和空间复杂度均为;另外,该方法独立于任何语种。     

邮件蠕虫传播与防御的分析研究*

邮件蠕虫利用e-mail在具有power-law结构特点的网络中进行传播,使得传统的蠕虫防御策略失效。结合power-law网络拓扑结构的特点,引入节点免疫和邮件服务器参与两种防御策略,分别对重复感染与非重复感染两种类型的邮件蠕虫传播进行了实验仿真。结果表明,节点的优先免疫类型、免疫起始时间、邮件服务器参与防御时间及蠕虫邮件识别正确率都与邮件蠕虫的传播有着紧密联系。     

一种新的邮件过滤技术研究

为了改进已有邮件过滤算法的不足之处,提出一种新的邮件过滤算法。以往的大部分过滤算法采用的都是邮件属性精确匹配算法,并未使用模糊匹配思想,因此邮件的漏检率较高,并且发现未知邮件的效率较低。针对以往邮件过滤算法中漏检率比较高的不足之处,文中提出的邮件过滤算法的改进思路是：首先使用传统的黑白名单过滤技术对已知的邮件进行分类,那些是正常邮件,那些是垃圾邮件。在此基础之上使用相似性算法来计算未知邮件与已知邮件的相似度,从而达到对未知邮件分类目的,最后使用MMTD算法对的邮件相似度的好坏做出衡量,并且以此为邮件过滤提供有效的依据,经过以上的三个步骤之后,最后进行邮件的过滤。     

您用“签字”和“名片”了吗?

您是否每天都要发送大量的邮件?是否每次发送邮件的时候都一次次的键入一些必要而又重复的信息?比如您的个人信息和礼仪用语等。如果已经厌倦了一次次重复输入,为什么不试试邮件程序提供     

基于贝叶斯算法和费舍尔算法的垃圾邮件过滤系统设计与实现

贝叶斯过滤算法和费舍尔过滤算法均是利用统计学知识对于垃圾邮件进行过滤的算法,有着良好的过滤效果。该文设计将某一词组(单词)出现概率使用加权计算的方法,改善了朴素贝叶斯算法和朴素费舍尔的邮件过滤算法对于出现较少的单词误判情况,使系统对于垃圾邮件判断的准确率上升。设计可以使用个性化的垃圾邮件过滤方案,支持使用邮件下载协议(POP3、IMAP协议)从邮件服务器下载邮件,以及使用邮件解析协议(MIME协议)对于邮件进行解析,支持邮件发送协议(SMTP协议)帮助用户发送邮件。     

一种改进的贝叶斯邮件过滤算法

贝叶斯过滤算法是反垃圾邮件过滤技术中应用最为广泛的方法之一。考虑到邮件的错误分类对邮件接收者带来的损失不同,引入判定垃圾邮件是判定正常邮件的λ倍作为最终邮件分类依据;同时,为了提高贝叶斯过滤算法的分类质量,运用遗传算法来对邮件中正文和标题的特征词在邮件分类中不同的重要程度做区分。最后用实际的邮件样本对改进后的算法进行验证,验证结果表明,利用遗传算法优化配合贝叶斯过滤算法能有效提高邮件分类的质量。     

基于用户行为的邮件分类算法

针对垃圾邮件过滤过程中分类模型难以个性化、难以适应用户兴趣动态变化的问题,提出了一种基于用户行为的邮件分类算法。通过分析朴素贝叶(NB)斯分类算法的原理,改造朴素贝叶斯算法,使其具有动态调整能力。邮件服务器接收到新邮件后自动进行分类判别,用户浏览邮件的过程中对邮件进行操作,根据用户对错分邮件的处理自动将该邮件加入训练数据集,并动态更新相应特征的统计概率,使邮件分类算法能够依据用户对不同邮件的操作行为动态调整分类模型,以达到有效过滤垃圾邮件的目的。与常用的贝叶斯分类算法的实验比较表明在给定小样本集合进行训练的情况下,新算法对于垃圾邮件的识别率比传统的朴素贝叶斯方法、基于风险敏感的朴素贝叶斯方法等提高了10%,获得了较好的分类性能。     

Web多媒体邮件中的编码解码方法研究

文章在对多媒体邮件标准和多媒体邮件编码方法进行研究的基础上,分析了中文Web多媒体邮件中出现的乱码问题,提出了中文多媒体邮件编码解码算法。列出了利用该算法在实现中文Web多媒体系统的过程中会碰到的一些问题,并给出了相应的解决策略。     

一种用于电子邮件分类的特征选择算法

邮件分类是当前研究的一个热点问题,而如何进行邮件特征选择,是邮件分类中的重要问题。在介绍几种常用的邮件分类的特征选择算法的同时,提出了将非搜索型算法FCBF与搜索型算法SFS结合的特征选择方法。实验验证了该方法的有效性和可行性,能够有效提高分类器的准确率。     

一种基于动态图像处理的邮件检测方法

利用动态图像处理技术,设计了一种实时处理的邮件检测算法。采用了一种快速的中值滤波算法进行降噪处理,提出了一种简洁的边界提取算法,可以快速地提取出单像素边界。然后以自适应多直线匹配的方法实现两帧图像的配准,并利用差分图像信息实现邮件图像的提取,最后计算出邮件的相关参数。仿真实验表明,该算法能有效检测出运动状态下邮件的相关参数,实时性良好。     

Context‐based email classification model

Context‐based email classification requires understanding of semantic and structural attributes of email. Most of the research has focused on generating semantic properties through structural components of email. By viewing emails as events (as a major subset of class of email), a rich contextual test‐bed representation for understanding of the semantic attributes of emails has been devised. The event‐ based emails have traditionally been studied based on simple structural properties. In this paper, we present a novel approach by first representing such class of emails as graphs, followed by heuristically applying graph mining and matching algorithm to pick templates representing contextual and semantic attributes that help classify emails. The classification templates used three key event classes: social, personal and professional. Results show that our graph mining and matching supported template‐based approach performs consistently well over event email data set with high accuracy.     

基于Mobile Agent的协作式反垃圾邮件系统设计

提出一种基于Mobile Agent技术的协作式反垃圾邮件体系结构,并对由代理服务器、反垃圾邮件客户端、动态负载均衡层、Mobile Agent层及反垃圾邮件服务器组成的五层结构进行阐述;代理服务器用来解决对邮件服务器的统一访问,降低由邮件服务器及客户端的多样性带来的系统复杂度;使用Nilsimsa算法实现相似邮件Hash过滤;最后对协作式反垃圾系统进行测试。     

基于邮件正文的邮箱用户别名抽取

邮箱用户身份信息挖掘是数据挖掘研究的一个热点。当前相关研究大多仅从邮件头中抽取邮箱用户的别名,遗漏了邮件正文中潜藏的更能代表通信双方身份的别名信息。针对纯文本邮件正文中邮箱用户别名信息抽取问题,提出了基于统计和规则过滤的称呼块和签名块定位算法,该算法能高效准确地从邮件正文中提取出蕴涵邮箱用户别名的称呼块和签名块文本片段;进一步提出了基于别名边界词汇模板修正的别名抽取方法,从而提高了仅基于命名实体识别或词性标注工具识别别名的准确率。实验结果表明,提出的方法可以有效地抽取出邮件正文中邮箱用户的别名。     

A multi-user perspective for personalized email communities

Email classification and prioritization expert systems have the potential to automatically group emails and users as communities based on their communication patterns, which is one of the most tedious tasks. The exchange of emails among users along with the time and content information determine the pattern of communication. The intelligent systems extract these patterns from an email corpus of single or all users and are limited to statistical analysis. However, the email information revealed in those methods is either constricted or widespread, i.e. single or all users respectively, which limits the usability of the resultant communities. In contrast to extreme views of the email information, we relax the aforementioned restrictions by considering a subset of all users as multi-user information in an incremental way to extend the personalization concept. Accordingly, we propose a multi-user personalized email community detection method to discover the groupings of email users based on their structural and semantic intimacy. We construct a social graph using multi-user personalized emails. Subsequently, the social graph is uniquely leveraged with expedient attributes, such as semantics, to identify user communities through collaborative similarity measure. The multi-user personalized communities, which are evaluated through different quality measures, enable the email systems to filter spam or malicious emails and suggest contacts while composing emails. The experimental results over two randomly selected users from email network, as constrained information, unveil partial interaction among 80% email users with 14% search space reduction where we notice 25% improvement in the clustering coefficient.     

企业内部邮件中话题讨论检索研究

随着信息技术的发展,企业检索已成为人们越来越关注的一个新的应用领域。作为企业检索的一个典型任务,企业内部的邮件检索是在企业中常常遇到的一个问题。企业内部存在着大量的可以公开访问的电子邮件,这些是企业重要的信息资源,如何高速有效地从这些邮件中检索到需要的信息具有很大意义。本文根据电子邮件本身具有的格式化特征和语义拓扑结构提出了基于电子邮件特征的检索模型。实验表明,该模型对电子邮件可以进行有效的检索,并且使用该模型在TREC2006电子邮件话题检索评测中取得了优异的性能成绩。     

Managing email overload with an automatic nonparametric clustering system

Email overload is a recent problem that there is increasingly difficulty that people have to process the large number of emails received daily. Currently, this problem becomes more and more serious and it has already affected the normal usage of email as a knowledge management tool. It has been recognized that categorizing emails into meaningful groups can greatly save cognitive load to process emails, and thus this is an effective way to manage the email overload problem. However, most current approaches still require significant human input for categorizing emails. In this paper, we develop an automatic email clustering system, underpinned by a new nonparametric text clustering algorithm. This system does not require any predefined input parameters and can automatically generate meaningful email clusters. The evaluation shows our new algorithm outperforms existing text clustering algorithms with higher efficiency and quality in terms of computational time and clustering quality measured by different gauges. The experimental results also well match the labeled human clustering results.

基于XML文本分类在邮件监控中的应用研究

邮件监控是网络信息安全的一个重要方面。而监控得到的邮件的处理是一项困难的工作。本文提出并实现了一种应用于邮件监控的邮件处理方式。首先将邮件转换为结构性较强的XML文档,然后通过搜索过滤方式得到初步邮件集,在此基础上对邮件的不同节点应用基于内容的文本分类进一步对邮件进行类别划分。实验证明,该处理方式是行之有效的。     

Social feature-based enterprise email classification without examining email contents

Without imposing restrictions, many enterprises find nonwork-related contents consuming network resources. Business communication over emails thus incurs undesired delays and inflicts damages to businesses, explaining why many enterprises are concerned with the competition to use email services. Obviously, enterprises should prioritize business emails over personal ones in their email service. Therefore, previous works present content-based classification methods to categorize enterprise emails into business or personal correspondence. Accuracy of these methods is largely determined by their ability to survey as much information as possible. However, in addition to decreasing the performance of these methods, monitoring the details of email contents may violate privacy rights that are under legal protection, requiring a careful balance of accurately classifying enterprise emails and protecting privacy rights. The proposed email classification method is thus based on social features rather than a survey of emails contents. Social-based metrics are also designed to characterize emails as social features; the obtained features are treated as an input of machine learning-based classifiers for email classification. Experimental results demonstrate the high accuracy of the proposed method in classifying emails. In contrast with other content-based methods that examine email contents, the emphasis on social features in the proposed method is a promising alternative for solving similar email classification problems.     

基于粗糙集的加权朴素贝叶斯邮件过滤方法

邮件过滤中有两个关键问题,一是如何选择有效的邮件特征集,二是设计较好的邮件过滤算法。在对邮件特性进行分析的基础上,综合邮件头及邮件内容的主要形象特征给出了一种新的邮件特征集提取方法。用粗糙集的信息观点度量了各属性的重要性,并以此为权重进行加权朴素贝叶斯垃圾邮件过滤,有效地解决了朴素贝叶斯分类中的条件依赖性问题。通过在中英文邮件集上的测试实验,证明了所提出的邮件过滤方法的有效性。     

Combining neural networks and semantic feature space for email classification

Email is one of the most ubiquitous and pervasive applications used on a daily basis by millions of people worldwide, individuals and organizations more and more rely on the emails to communicate and share information and knowledge. However, the increase in email users has resulted in a dramatic increase in spam emails during the past few years. It is becoming a big challenge to process and manage the emails efficiently for and individuals and organizations. This paper proposes new email classification models using a linear neural network trained by perceptron learning algorithm and a nonlinear neural network trained by back-propagation learning algorithm. An efficient semantic feature space (SFS) method is introduced in these classification models. The traditional back-propagation neural network (BPNN) has slow learning speed and is prone to trap into a local minimum, so the modified back-propagation neural network (MBPNN) is presented to overcome these limitations. The vector space model based email classification system suffers from a large number of features and ambiguity in the meaning of terms, which will lead to sparse and noisy feature space. So we use the SFS to convert the original sparse and noisy feature space to a semantically richer feature space, which will helps to accelerate the learning speed. The experiments are conducted based on different training set size and extracted feature size. Experimental results show that the models using MBPNN outperform the traditional BPNN, and the use of SFS can greatly reduce the feature dimensionality and improve email classification performance.