基于QMAIL-LDAP的反垃圾邮件系统

互联网上垃圾邮件问题日益严重,有必要在邮件系统上实施垃圾邮件防范措施。该文分析了垃圾邮件的起因和特点,提出了垃圾邮件的防范措施和基于QMAIL-LDAP的反垃圾邮件系统,阐述了该系统的体系结构及其实现方法,以实际运行数据说明了系统对抗垃圾邮件的有效性。     

Control of spam: a comparative approach with special reference to India

Spam and other related online threats are a real concern to all Internet users as they can lead to online fraud involving counterfeit websites, the theft of personal data such as credit card information, personal information through illicit access to computer systems and false or misleading representations in the market place. These are not imaginary threats but are supported by empirical evidence. This wastes time to users opening spam with misleading subject lines, wastes Internet resources trying to block it, consumes storage and bandwidth, and costs money in fighting it. E-commerce cannot flourish in an uncertain environment where security of personal data is not guaranteed. With the aim of boosting confidence in online commerce and protecting the interests of consumers, governments have introduced anti-spam legislation to protect consumers and businesses. This legislation has introduced different parameters. However, legislation alone cannot prevent spam. It is to be combined with technological measures. There is no separate legislation in India to deal with this problem. This paper analyses technological solutions and legal measures in the light of trans-national developments to provide background for any judicial decision making or legislative policy in India.     

Effective scheduling strategies for boosting performance on rule-based spam filtering frameworks

Despite the enormous importance of e-mail to current worldwide communication, the increase of spam deliveries has had a significant adverse effect for all its users. In order to adequately fight spam, both the filtering industry and scientific community have developed and deployed the fastest and most accurate filtering techniques. However, the increasing volume of new incoming messages needing classification together with the lack of adequate support for anti-spam services on the cloud, make filtering efficiency an absolute necessity. In this context, and given the extensive utilization and increasing significance of rule-based filtering frameworks for the anti-spam domain, this work studies and analyses the importance of both existing and novel scheduling strategies to make the most of currently available anti-spam filtering techniques. Results obtained from the experiments demonstrated that some scheduling alternatives resulted in time savings of up to 26% for filtering messages, while maintaining the same classification accuracy.     

基于PKI体系的安全电子邮件系统的研究与设计

电子邮件发展出现的问题主要表现为机密泄露、信息欺骗、病毒侵害、垃圾邮件等.相关机构对此现象做出了一些针对性的解决方案,如电子签名法、反垃圾邮件等与电子邮件安全相关法律法规的制定.安全电子邮件系统的建立是在普通电子邮件系统的基础上来实现的,是与PKI与之相关联的CA认证、数字证书、加密、数字签名等技术相结合而产生的,还利用了综合防病毒技术、反垃圾邮件技术,从而给使用电子邮件的用户创造了一个安全的使用环境.     

反垃圾邮件新技术

电子邮件是我们最常是用的网络应用技术之一,而垃圾邮件问题也日益成为主要的安全问题,该文就传统的和新近出现的反垃圾邮件技术做了一个讲解。     

基于颜色与角点特征的图像垃圾邮件识别算法

垃圾邮件制造者将垃圾信息嵌入图像中,使基于文本内容的反垃圾邮件系统失效。对垃圾邮件图像的特点深入分析后,提出一种垃圾邮件图像识别算法。垃圾邮件图像多为计算机合成图像,其颜色不如自然图像丰富,且因含有大量文字导致图像中角点角度值分布呈现出一定的规律性。针对此问题选用颜色和角点特征并结合支持向量机分类算法来识别垃圾邮件图像。实验结果表明,该算法对真实垃圾邮件图像的识别精确率超过98％。     

应用行为模式识别的垃圾邮件过滤技术

近年来,垃圾邮件在互联网的肆虐程度逐渐加强,据分析,垃圾邮件竟占到互联网总流量的五分之一左右,垃圾邮件产生的巨大的存储需求,极大地威胁了信息安全系统的有效性。垃圾邮件具有多种多样的内容形式,根据互联网安全体系的要求,基于垃圾邮件模式识别技术正日益凸显其功效,有望成为一种简洁可行的反垃圾邮件解决方式。     

针对基于内容邮件过滤器的攻击及过滤改进

基于内容的过滤技术是反垃圾邮件技术中最有效的方法,但是垃圾邮件发送者千方百计利用各种方法对基于内容的垃圾邮件过滤器进行攻击,严重影响了过滤器的正确率和健壮性.在介绍主要基于内容的垃圾邮件过滤技术基础上,分析了针对基于内容垃圾邮件过滤器的常用攻击方法,并提出了相应的过滤改进技术.同时,针对单词沙拉攻击,在几种过滤器上进行了模拟攻击实验.最后分析了垃圾邮件技术的发展趋势和未来反垃圾邮件技术的主要改进方法.     

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.     

Training SVM email classifiers using very large imbalanced dataset

The Internet has been flooded with spam emails, and during the last decade there has been an increasing demand for reliable anti-spam email filters. The problem of filtering emails can be considered as a classification problem in the field of supervised learning. Theoretically, many mature technologies, for example, support vector machines (SVM), can be used to solve this problem. However, in real enterprise applications, the training data are typically collected via honeypots and thus are always of huge amounts and highly biased towards spam emails. This challenges both efficiency and effectiveness of conventional technologies. In this article, we propose an undersampling method to compress and balance the training set used for the conventional SVM classifier with minimal information loss. The key observation is that we can make a trade-off between training set size and information loss by carefully defining a similarity measure between data samples. Our experiments show that the SVM classifier provides a better performance by applying our compressing and balancing approach.     

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。     

一种新型的基于URL过滤的反垃圾邮件技术*

在深入分析了传统垃圾邮件过滤技术不足的基础上,提出并实现了一种新型的基于URL过滤的垃圾邮件过滤技术(URLbased spamfiltering,UBSF)。该方法通过对比从到来邮件中提取的URL与URL库中存储的URL信息的相似性来判定垃圾邮件。通过语料库以及构建实际系统原型的测试,表明该方法具有准确性高、误报率低以及实时处理速度快的优点。     

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。     

反垃圾邮件技术及其最新展望

随着Internet技术的迅猛发展,垃圾邮件日益猖獗,本文简单阐述了垃圾邮件的定义和危害,并详细介绍了现今常用的传统反垃圾邮件技术及其综合运用.最后,针对近来图片垃圾邮件猛增的最新形势,对未来反垃圾邮件技术的发展进行了展望.     

基于语义网格的反垃圾邮件应用架构设计

目前对垃圾邮件问题的大部分解决方法仍受到互联网自身特性的限制,而且在较大范围内邮件过滤的准确率也不能满足人们的要求.因此从语义网格的角度出发,利用语义网格的语义分析能力强且具有网格优势等特点来解决垃圾邮件问题.提出反垃圾邮件语义网格的概念,并详细阐述了反垃圾邮件语义网格系统的基本思想、体系结构以及支撑技术;利用OptorSim网格模拟器对系统进行测试;对系统中一些关键问题进行讨论.     

基于用户反馈的反垃圾邮件技术

在分析传统垃圾邮件过滤技术的基础上,提出了一种基于用户反馈的反垃圾邮件技术。该技术通过引入用户反馈机制,使用改进的朴素贝叶斯方法,构建面向特定用户的过滤器,从而进行垃圾邮件过滤。邮件语料库实验和原型系统的测试证明,该方法能够有效地降低误报率,提高反垃圾邮件系统的可用性,具有较好的实用效果。     

反垃圾邮件网格过滤系统的设计与实现

文章开始给出了网格和垃圾邮件的定义,在比较分析了国内外反垃圾邮件的关键技术的基础上,提出了在网格范围内进行垃圾邮件过滤的管理系统的设计方案,着重讨论了过滤器的选取和基于内容的贝叶斯过滤算法的实现。     

基于校园内网的反垃圾邮件技术的研究

随着校园网信息化的进程不断的加快,越来越多的学校搭建了自己的邮件系统,提供给教师或学生使用,系统除了SMTP会话层的反垃圾邮件措施,SurfControl邮件安全信息网关RiskFilter在应用层上,反垃圾邮件引擎ASA集成了四种反垃圾邮件技术:数字指纹、启发式分析、语义分析和Internet威胁库,由SurfControl全球众多内容安全专家,365x7x24小时不间断维护更新ASA数据库,每天自动更新4次,提供准确率高于99.2%、误判率低于0.1%的垃圾邮件过滤效果。该文通过对垃圾邮件的处理办法进行比较,结合校园邮件的特点,提出了有关实际应用的解决方案,以便更好地满足校园网的邮件系统的需求。     

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.