一种互联网垃圾邮件综合过滤方案

垃圾邮件是互联网上亟待解决的问题。介绍了几种典型的垃圾邮件过滤技术,提出了一种结合邮件过滤和病毒检测技术、可以个性化定制过滤需求的综合过滤方案。相比于已有的方案,文中提出的方案具有同时检测病毒、过滤垃圾邮件和个性化过滤的优点,可以更加有效地解决邮件安全和个性化过滤的问题。     

一种互联网垃圾邮件综合过滤方案

垃圾邮件是互联网上亟待解决的问题。介绍了几种典型的垃圾邮件过滤技术，提出了一种结合邮件过滤和病毒检测技术、可以个性化定制过滤需求的综合过滤方案。相比于已有的方案，文中提出的方案具有同时检测病毒、过滤垃圾邮件和个性化过滤的优点，可以更加有效地鼹决邮件安全和个性化过滤的问题。     

基于DKIM和评分管理的反邮件系统的设计

垃圾邮件的泛滥带来了各种各样的问题,尤其在安全方面.如何减少垃圾邮件也是很值得去研究的课题.文中的主要目的就是探索一种更加有效的拦截垃圾邮件的方法.在文中,首先简要地介绍了两种不同的反垃圾邮件技术,结合DKIM和基于内容的过滤技术的优点,设计出了一个反垃圾邮件的方案.方案中采用评分管理的方法来实现对邮件内容的过滤,可以取得与ID3算法一致的结果.因此,通过将现有的反邮件技术的有效整合,可以取得有效的垃圾邮件过滤效果.     

基于DKIM和评分管理的反邮件系统的设计

垃圾邮件的泛滥带来了各种各样的问题,尤其在安全方面。如何减少垃圾邮件也是很值得去研究的课题。文中的主要目的就是探索一种更加有效的拦截垃圾邮件的方法。在文中,首先简要地介绍了两种不同的反垃圾邮件技术,结合DKIM和基于内容的过滤技术的优点,设计出了一个反垃圾邮件的方案。方案中采用评分管理的方法来实现对邮件内容的过滤,可以取得与ID3算法一致的结果。因此,通过将现有的反邮件技术的有效整合,可以取得有效的垃圾邮件过滤效果。     

一个基于Naive Bayesian垃圾邮件过滤器的改进

近几年来,垃圾邮件成为互联网的公害之一。现有的反垃圾邮件技术中,基于统计方法的Naive Bayesian分类算法在垃圾邮件过滤中有很好的效果。文中简单介绍了Naive Bayesian分类算法,提出了一种旨在提高垃圾邮件过滤精确率的改进方案,并给出了实验结果。     

一个基于Naive Bayesian垃圾邮件过滤器的改进

近几年来，垃圾邮件成为互联网的公害之一。现有的反垃圾邮件技术中，基于统计方法的Naive Bayesian分类算法在垃圾邮件过滤中有很好的效果。文中简单介绍了Naive Bayesian分类算法，提出了一种旨在提高垃圾邮件过滤精确率的改进方案，并给出了实验结果。     

基于网格的垃圾邮件过滤系统

目前实际应用的垃圾邮件过滤技术效果不太理想,尤其是对垃圾邮件的误判率和漏判率问题较为突出.其中,基于概率统计的简单贝叶斯分类算法相对而言效果较好.为提高垃圾邮件过滤系统的分类准确率和效率,利用网格技术资源高度共享的优势,并对Bayes分类算法的应用模式进行改进,提出了一种基于网格的垃圾邮件过滤系统方案.     

基于蜜罐技术的垃圾邮件搜集系统设计与实现

面对当今猖狂的垃圾邮件发展事态,反垃圾邮件机制即邮件过滤技术也逐渐成为信息安全的焦点.而邮件过滤技术性能的好坏,关键在于对大量垃圾邮件样本的收集、学习与分析.该系统运用C#语言工具设计开发了具有蜜罐特征的垃圾邮件样本采集系统,实现了高效生成蜜罐邮箱、暴露蜜罐邮箱、查看样本信息的一体化功能.并且用户能够根据需求,自我配置垃圾邮件样本采集方案,能够随时生成蜜罐邮箱,并按照方案进行暴露,大大提高了垃圾邮件样本收集的时效性和广泛性,为各类垃圾邮件过滤机制提供多样的、新鲜的样本学习资料.     

垃圾邮件过滤技术研究综述*

垃圾邮件过滤是一种主动安全防御技术。首先概述了垃圾邮件过滤的发展历史及其基本概念;然后根据不同的标准对垃圾邮件过滤技术进行了分类,并评述了各种垃圾邮件过滤方法和技术;最后展望了垃圾邮件过滤技术及其产品的发展方向。     

对垃圾邮件过滤技术的问题研究

近年来,电子邮件方便人们的生活,同时,也有大量的垃圾邮件不断涌现,随之也出现了各种垃圾邮件过滤技术。文中主要介绍基于信件源垃圾邮件过滤技术和基于内容的垃圾邮件过滤技术,通过对这两种技术的介绍,分析了垃圾邮件过滤技术的优缺点,并对垃圾邮件过滤技术中存在的问题进行了讨论。垃圾邮件发送者不断改变发送策略以逃避过滤技术的过滤,垃圾邮件发送策略也不断的更新。文中对近年来垃圾邮件发送的新策略进行了详细的阐述,讨论目前垃圾邮件过滤技术研究中遇到的问题和挑战。     

SpaDeS: Detecting spammers at the source network

Despite the large variety and wide adoption of different techniques to detect and filter unsolicited messages (spams), the total amount of such messages over the Internet remains very large. Some reports point out that around 80% of all emails are spams. As a consequence, significant amounts of network resources are still wasted as filtering strategies are usually performed only at the email destination server. Moreover, a considerable part of these unsolicited messages is sent by users who are unaware of their spamming activity and may thus inadvertently be classified as spammers. In this case, these oblivious users act as spambots, i.e., members of a spamming botnet. This paper proposes a new method for detecting spammers at the source network, whether they are individual malicious users or oblivious members of a spamming botnet. Our method, called SpaDeS, is based on a supervised classification technique and relies only on network-level metrics, thus not requiring inspection of message content. We evaluate SpaDeS using real datasets collected from a Brazilian broadband ISP. Our results show that our method is quite effective, correctly classifying the vast majority (87%) of the spammers while misclassifying only around 2% of the legitimate users.     

Russian zombie army turned back

US government and industry experts voiced fears on the week ending 25 June of an impending massive attack that combined hacking, malcode-injection, and spamming.     

邮件系统防止垃圾邮件攻击的安全方案的探讨

描述了邮件协议的工作原理，分析了可能出现垃圾邮件攻击的网络安全漏洞，对于目前主流邮件服务器系统Send-mail，Qmail，Lotus Domino，Exchange出现垃圾邮件主要原因、防止垃圾邮件的安全方案进行了探讨，并提出各系统相应的具体解决方案，这些方案在实际使用中取得较好的效果。     

CanarDeep: a hybrid deep neural model with mixed fusion for rumour detection in social data streams

Neural Computing and Applications - The unrelenting trend of doctored narratives, content spamming, fake news and rumour dissemination on social media can lead to grave consequences that range from...     

Web concerns [legal aspects]

Recently, misuse of the Web seems to be cropping up everywhere. The author discusses three cases of misuse causing concern to many Web users: spamming, meta tagging, and keying advertising to searching     

Robust classification for spam filtering by back-propagation neural networks using behavior-based features

Earlier works on detecting spam e-mails usually compare the contents of e-mails against specific keywords, which are not robust as the spammers frequently change the terms used in e-mails. We have presented in this paper a novel featuring method for spam filtering. Instead of classifying e-mails according to keywords, this study analyzes the spamming behaviors and extracts the representative ones as features for describing the characteristics of e-mails. An back-propagation neural network is designed and implemented, which builds classification model by considering the behavior-based features revealed from e-mails’ headers and syslogs. Since spamming behaviors are infrequently changed, compared with the change frequency of keywords used in spams, behavior-based features are more robust with respect to the change of time; so that the behavior-based filtering mechanism outperform keyword-based filtering. The experimental results indicate that our methods are more useful in distinguishing spam e-mails than that of keyword-based comparison.     

基于链接分析的重要Blog信息源发现

本文提出了一种基于链接分析的对Blog信息源进行量化评估的方法,在此基础之上发现重要Blog信息源,既体现了Blog信息的特点,又在一定程度上减小了作弊链接对链接分析结果的影响,能为用户阅读信息提供方便,并可望为Blog信息检索提供一种新的思路。为了证明该评估方法的有效性,本文还提出了Blog信息源重要性的评价指标,对比了重要Blog信息源量化评估方法和评价指标的评分结果,通过相关性分析,表明此方法和评价指标存在高度的一致性。     

What’s in a name. . . generator?

Domain generation algorithms can be used for registering spamming and phishing sites, as well as by botnets for domain flux. In this paper we study Kwyjibo, a more sophisticated domain/word generation algorithm that is able to produce over 48 million distinct pronounceable words. We show through four different implementations how Kwyjibo might be deployed and how its size can be reduced to under 163KiB using a technique we call ??lossy distribution compression??. This means that Kwyjibo is both powerful as well as small enough to be used by malware on mobile devices.     

A novel ensemble decision tree based on under-sampling and clonal selection for web spam detection

Currently, web spamming is a serious problem for search engines. It not only degrades the quality of search results by intentionally boosting undesirable web pages to users, but also causes the search engine to waste a significant amount of computational and storage resources in manipulating useless information. In this paper, we present a novel ensemble classifier for web spam detection which combines the clonal selection algorithm for feature selection and under-sampling for data balancing. This web spam detection system is called USCS. The USCS ensemble classifiers can automatically sample and select sub-classifiers. First, the system will convert the imbalanced training dataset into several balanced datasets using the under-sampling method. Second, the system will automatically select several optimal feature subsets for each sub-classifier using a customized clonal selection algorithm. Third, the system will build several C4.5 decision tree sub-classifiers from these balanced datasets based on its specified features. Finally, these sub-classifiers will be used to construct an ensemble decision tree classifier which will be applied to classify the examples in the testing data. Experiments on WEBSPAM-UK2006 dataset on the web spam problem show that our proposed approach, the USCS ensemble web spam classifier, contributes significant classification performance compared to several baseline systems and state-of-the-art approaches.