电子邮件目录收割攻击的防御

为了更好地解决垃圾邮件的问题,提高对垃圾邮件的防御效果,本文从造成垃圾邮件的其中一个原因———子邮件目录收割攻击(DHA)入手,通过对DHA攻击原理的分析,提出基于黑名单同时以邮件地址阈值和IP地址阈值为锁定条件的防御策略,并在攻击资源有限的条件下对防御策略进行模拟测试。分析结果表明该防御策略能对DHA进行有效的防御,同时得出防御策略中的过滤阈值和锁定时间的设置是防御DHA的关键点。     

实用高效的垃圾邮件过滤算法

为了提高电子邮件中垃圾邮件的过滤准确率和效率,以朴素贝叶斯算法和K最近邻（KNN：K-Nearest Neighbors）算法为基础,对传统垃圾邮件过滤算法进行改进,给出邮件的合法属性和非法属性的概念,并提出一种新的分类算法——基于邮件合法属性和非法属性的分类算法（SEASF：Simple and Efficient Algorithm to Spam Filter based on legitimate attribute and nonlicet attribute）。SEASF计算复杂度较低,可适用于大规模场合及邮件的在线过滤。将SEASF算法应用于垃圾邮件过滤的结果表明,该算法可大幅度提高分类精度,分类速度也令人满意。     

A Survey on Fast-flux Attacks

ABSTRACT

“Fast-flux” refers to rapidly assigning different IP addresses to the same domain name. Although there are some legitimate uses for this technique, recently it has become a favorite tool for cyber criminals to launch collaborative attacks. After it was first observed by Honeynet, it was reported that fast-flux has been used in phishing, malware spreading, spam, and other malicious activities linked to criminal organizations. Combining with peer-to-peer networking, distributed command and control, web-based load balancing, and proxy redirection, fast-flux makes Internet attacks more resistant to discovery and counter-measure. This article aims at giving a comprehensive survey on fast-flux attacks. Some important issues including technical background, classification, characterization, measurement and detection, and mitigation are discussed. Challenges of detecting and mitigating fast-flux attack are also pointed out.     

基于网页抓取分析和统计压缩模型的垃圾邮件过滤系统研究

垃圾邮件过滤是当前计算机领域的热点问题。文章针对目前网页抓取分析技术不能深入分析网页内容的缺点,提出了一种优化的网页抓取分析技术,能够对网页提取一些更为深入的特征,并以此为基础,完成了基于网页抓取分析和统计压缩模型的垃圾邮件过滤系统的设计与实现。文章创新地提取出5种新的特征,实验结果表明,这些特征对于增高TPR(True Positive Rate,真正类率),降低FPR(False Positive Rate,负正类率),提升垃圾邮件过滤的效率和准确性具有显著作用。     

基于组合算法的中文反垃圾邮件分类系统的研究*

论述了一种采用组合算法实现的垃圾邮件分类系统,并在Windows平台下用Visual Basic 6.0实现。本系统工作在邮件客户端,基于邮件内容的解析,相对于只使用基于分类器的垃圾邮件分类系统,不仅能有效快速地分类邮件,同时提高了分类的精度、降低误判率。     

Stochastic Gradient Boosting Model for Twitter Spam Detection

In today’s world of connectivity there is a huge amount of data than we could imagine. The number of network users are increasing day by day and there are large number of social networks which keeps the users connected all the time. These social networks give the complete independence to the user to post the data either political, commercial or entertainment value. Some data may be sensitive and have a greater impact on the society as a result. The trustworthiness of data is important when it comes to public social networking sites like facebook and twitter. Due to the large user base and its openness there is a huge possibility to spread spam messages in this network. Spam detection is a technique to identify and mark data as a false data value. There are lot of machine learning approaches proposed to detect spam in social networks. The efficiency of any spam detection algorithm is determined by its cost factor and accuracy. Aiming to improve the detection of spam in the social networks this study proposes using statistical based features that are modelled through the supervised boosting approach called Stochastic gradient boosting to evaluate the twitter data sets in the English language. The performance of the proposed model is evaluated using simulation results.

     

Using new scheduling heuristics based on resource consumption information for increasing throughput on rule‐based spam filtering systems

The large increase of spam deliveries since the first half of 2013 entailed hard to solve troubles in spam filters. In order to adequately fight spam, the throughput of spam filtering platforms should be necessarily increased. In this context, and taking into consideration the widespread utilization of rule‐based filtering frameworks in the spam filtering domain, this work proposes three novel scheduling strategies for optimizing the time needed to classify new incoming e‐mails through an intelligent management of computational resources depending on the Central Processing Unit (CPU) usage and Input/Output (I/O) delays. In order to demonstrate the suitability of our approaches, we include in our experiments a comparative study in contrast to other successful heuristics previously published in the scientific literature. Results achieved demonstrated that one of our alternative heuristics allows time savings of up to 10% in message filtering, while keeping the same classification accuracy. Copyright © 2015 John Wiley & Sons, Ltd.     

用双层减样法优化大规模SVM垃圾标签检测模型*

针对支持向量机在训练大规模数据集时出现的速度瓶颈问题,提出一种新的减样方法,称双层减样法。数据减样时,双层减样法从粗、细粒度两个层次削减样本。粗粒度约减时,利用核空间距离聚类法,以簇为单位削减冗余子集。细粒度约减时,以点为单位挑选剩余点集中的支持向量。实验表明,双层减样法能有效的压缩样本数据,同时还能放大数据集的分类特征,提高分类器的分类精度。将此法应用于大规模SVM垃圾标签检测模型的训练集优化上,能明显提高检测模型的训练速度。双层减样法是将“粒度”和“层次”的概念引入减样法中,在约减时适时改变约减幅度。这比传统减样法更具有优势。     

Content-based mobile spam classification using stylistically motivated features

The feature of brevity in mobile phone messages makes it difficult to distinguish lexical patterns to identify spam. This paper proposes a novel approach to spam classification of extremely short messages using not only lexical features that reflect the content of a message but new stylistic features that indicate the manner in which the message is written. Experiments on two mobile phone message collections in two different languages show that the approach outperforms previous content-based approaches significantly, regardless of language.     

面向垃圾邮件过滤的典型机器学习算法比较研究

基于机器学习的垃圾邮件过滤技术是当前垃圾邮件过滤的主流方法。机器学习模型主要分为两类：以朴素贝叶斯（NB）为代表的生成模型和以逻辑回归模型（LR）、支持向量机模型（SVM）为代表的判别学习模型。以往对两种模型的研究都是针对某一种语言进行,对于模型的语言独立性与相关性研究较少。因此,在中文数据集和英文数据集上比较典型的生产模型和判别学习模型的过滤性能。比较Bogo（Bogo系统是基于贝叶斯算法的,它是典型的生成模型）、逻辑回归模型和松弛在线支持向量机（两种典型的判别学习模型）在中英文数据集上的过滤性能。其中：实验是在公开英文数据集TREC05p-1、TREC06p和公开中文数据集TREC06c、SEWM2011上进行。实验结果显示基于判别模型垃圾邮件过滤器性能明显优于基于生成模型,并且相同的模型在中文数据集上显示了较好的效果。