一个可靠的基于组件的电子邮件过滤体系结构

W. Gansterer, A. Janecek, P. Lechner
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引用次数: 8

摘要

介绍了一种用于分类和过滤未经请求的批量和商业电子邮件(“垃圾邮件”)的三组件体系结构。第一个组件是灰列表的一个增强的自学习变体,它为下面的特征提取和分类组件奠定了基础。通过灰列表组件对所选消息的临时拒绝,在消息被接受并传递给最终收件人之前,可以对电子邮件内容进行“离线”深入检查。在特征提取组件中,为每个新到达的电子邮件确定一组特征。然后将这些特性用于在分类引擎中对消息进行分类,分类引擎包含对向量空间模型的适应。基于该模型,研究了一种用于垃圾邮件过滤的潜在语义索引的实现。所提出的体系结构有助于实现最小化垃圾邮件造成的资源浪费的目标,并且能够通过特征提取和分类组件的调整对高负载情况(包括DoS攻击)做出反应
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Reliable Component-Based Architecture for E-Mail Filtering
A three-component architecture for the classification and filtering of unsolicited bulk and commercial e-mail ("spam") is introduced. The first component, an enhanced self-learning variant of greylisting, sets the stage for the following feature extraction and classification components. Through the temporary rejection of selected messages by the greylisting component time becomes available for an "offline" in-depth examination of the e-mail content before the message is accepted and delivered to the final recipient. Within the feature extraction component a set of features for each newly arriving e-mail message is determined. These features are then used for the categorization of a message within the classification engine, which contains the adaptation of a vector space model. Based on this model, an implementation of latent semantic indexing for spam filtering is investigated. The architecture proposed contributes to the goal of minimizing the waste of resources caused by spam and is able to react to high load situations (including DoS attacks) via adaptations in the feature extraction and classification components
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