基于NDIS的Web邮件监控

本文基于NDIS技术,设计并实现了一个Windows下实时监控发送和接收Web邮件内容的邮件监控系统,提出通过邮件的内在标识ID将多次传输的TCP连接组合成一封邮件,解决了一封邮件分多次TCP传输的难题。实验测试结果表明该系统能准确监控到网络中发送和接收的Web邮件。     

基于WinPcap的邮件监控系统的设计与实现

针对目前广泛应用的邮件通信的安全问题，设计并实现了一种基于WinPcap的网络邮件监控系统。简单介绍了邮件监控的相关技术以及该系统的设计结构，对实现过程中涉及到的几个关键技术进行了详细阐述。最后进行了性能测试，测试结果验证了该系统对邮件的监控效果。     

邮件审计系统的设计与实现

针对目前邮件通信的安全问题,在深入研究邮件协议的基础上,提出一种基于libnids动态库的邮件监控系统的设计方案。文中对系统中涉及的几个关键技术进行深入阐述,实现了局域网内邮件的监控还原、关键词审计等功能。最后对系统的性能进行测试,测试结果表明该系统的监控效果能为网络管理部门提供一个高效的邮件监管平台。     

CPU占用100%的情况详解

在使用 Windows XP 操作系统的时候,经常出现 CPU 占用100%的情况,主要问题可能发生在下面的某些方面:防杀毒软件造成故障由于新版的 KV、金山、瑞星都加入了对网页、插件、邮件的随机监控,无疑增大了系统负担。处理方式:尽量使用最少的监控服务,或者升级你的硬件配置。驱动没有经过认证,造成 CPU 资源占用100%大量的测试版的驱动在网上泛滥,造成了难以发现的故障原因。处理方     

基于Linux防火墙的内部邮件监控系统

给出了一种分布式邮件监控系统的设计和实现方案.该方案的实现建立在Linux防火墙的基础上.采用了Netfilter构架中的ip queue机制获取流经网关的邮件,根据SMTP和POP3的协议特点提取邮件内容,利用文本分类技术对邮件内容进行监控.系统中广泛地使用了插件机制,明确地划分了实时处理和离线分析两大类操作的界限.针对文本分类器的特点,系统定义了简明的接口,使不同算法的分类器可以方便地整合到系统当中来.该方案的实施可以有效地监控流经网关的邮件.     

为可疑邮件贴标签

Equinix是一个数据中心(支持所有网络)和互联网交换服务的提供商。Equinix新加坡公司一直在寻求一种整合了反病毒和反垃圾邮件功能的解决方案,以保护用户的企业邮件服务。虽然Equinix已经在使用反病毒解决方案,但仍希望为其在新加坡地区的12000名邮件业务用户提供组合的反病毒和反垃圾邮件保护,主要标准包括垃圾邮件检测的准确度和对可疑邮件的有效管理。Equinix最终选择了Sophos PureMessage,不仅因为它能满足要求,还能帮助Equinix调优个别客户的垃圾邮件处理方式。Sophos PureMessage选择只对垃圾邮件可能性超过80%的消息加贴标签,由…     

综合邮件处理系统

邮件自动处理方式已发展为三种独立的自动化形式，最近，又形成了多种综合处理的模式。本文把一些综合处理方式与先进的独立处理方式相对比，讨论各种处理方法的可行性。     

在Outlook中设置无主题邮件的管理规划

我想在Outlook 2003中设置一条邮件管理规则。将收到的无主题邮件自动视作垃圾邮件来处理，请问有什么好办法？在发送邮件时．如果主题栏是空着的。Outlook将会弹出一个警告，提示用户必须键入主题，否则邮件将无法发送出去。碰到这类情况时。我一般都在邮件主题栏中键入一对双引号。引号内没有任何内容，就可以通过Outlook的审核。将邮件发送出去。可仔细一想。之前设置管理规则似乎又失去了意义。所以。我想知道在设置规则时。能否指定空白主题栏的邮件的处理方式?(重庆董坤)     

发送重要邮件 提醒、跟踪并举

邮件发送出去，总给人石沉大海的感觉，对方有没有阅读，什么时间阅读的，发送者一概不知，这对非常重要的邮件来说是不能接受的。其实，对于非常重要的邮件，我们可以通过一定的途径来提醒对方，并及时监控邮件的阅读状态。     

做一个邮件自动分检机

电子邮件给我们的信息交流带来了革命性的变化,但你是否也感受到它给你带来的烦恼呢?收发的邮件越来越多,给信件的管理带来了不小的麻烦,别急,为方便你对信件日常管理及阅读,Foxmail为你提供了邮件过滤功能。它可按照邮件的收件人、发件人、主题、邮件正文等条件对邮件进行过滤,对符合相应条件的邮件进行转移、拷贝、自动回复、等多种不同处理方式,从而使得邮件能够按照用户的要求自动分检,极大的方便用户对邮件的管理。急了吧！想知道如何分检的?跟我来吧！     

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.     

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.     

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.     

Email mining: tasks,common techniques,and tools

Email is one of the most popular forms of communication nowadays, mainly due to its efficiency, low cost, and compatibility of diversified types of information. In order to facilitate better usage of emails and explore business potentials in emailing, various data mining techniques have been applied on email data. In this paper, we present a brief survey of the major research efforts on email mining. To emphasize the differences between email mining and general text mining, we organize our survey on five major email mining tasks, namely spam detection, email categorization, contact analysis, email network property analysis and email visualization. Those tasks are inherently incorporated into various usages of emails. We systematically review the commonly used techniques and also discuss the related software tools available.     

Decisional style, mood and work communication: email diaries

To understand the use of technology to support interpersonal interaction, a theory of decisional style was applied to email use within the workplace. Previous research has used self-report and rating scales to address employee email behaviours, but this falls short of management's capability to monitor the actual behaviour. Thirty-nine employed individuals completed a five-day communication diary recording their actual behaviour upon receiving personal and work-related emails as well as the Melbourne Decision Making Questionnaire and the Depression Anxiety Stress Scales. It was found that vigilant individuals were more likely to use email in an efficient manner by deleting personal email and being less likely to open email later. Procrastinators, buckpassers and people experiencing high levels of negative affect were all more likely to delay dealing with email, which could be viewed as dealing with email in a less efficient manner. STATEMENT OF RELEVANCE: This work offers insights as to how people receive and process emails and is thus relevant to the development and implementation of collaborative technologies. Whilst other studies use individual's self-reports, this study uses a more accurate communication diary. Decisional style can predict the monitoring and response to electronic communication.     

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。     

基于改进向量空间模型的邮件分类

基于内容的邮件分类一般采用向量空间模型来表示邮件，该模型只是基于独立词在邮件内容中出现的频率来建立的，而并未考虑邮件的结构特征和词所在的上下文环境，这使得特征向量不能准确地表示邮件的内容，从而导致分类不够准确。文中提出了改进的向量空间模型，针对邮件特有的结构，以段落为分块单位，通过分析段落间的关系和段落中的内容来更改特征词的权重。以此模型设计了一个邮件分类系统，并对该系统进行了测试和结果分析。     

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.

基于图像信息度量与关键词的邮件智能分类系统

如何利用邮件的正文与附件信息有效地实现其分类，是现在邮件处理领域一个重要的课题。该文从商业应用角度提出了一种基于图像信息度量与关键词的邮件智能过滤与分发方法，通过基于朴素贝叶斯分类器的邮件关键词信息处理，及附件图像信息的基于归一化PIM文本图像检测理论的分析，能够综合运用邮件正文、地址等文本信息与附件图像信息作为分类的评价参数，有效地实现了邮件的智能分类。