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Study on the Effectiveness of Spam Detection Technologies

机译:垃圾邮件检测技术的有效性研究

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Nowadays, spam has become serious issue for computer security, because it becomes a main source for disseminating threats, including viruses, worms and phishing attacks. Currently, a large volume of received emails are spam. Different approaches to combating these unwanted messages, including challenge response model, whitelisting, blacklisting, email signatures and different machine learning methods, are in place to deal with this issue. These solutions are available for end users but due to dynamic nature of Web, there is no 100% secure systems around the world which can handle this problem. In most of the cases spam detectors use machine learning techniques to filter web traffic. This work focuses on systematically analyzing the strength and weakness of current technologies for spam detection and taxonomy of known approaches is introduced.
机译:如今,垃圾邮件已成为计算机安全性的严重问题,因为它成为传播威胁的主要来源,包括病毒,蠕虫和网络钓鱼攻击。目前,大量收到的电子邮件是垃圾邮件。对抗这些不需要的消息的不同方法,包括质询响应模型,白名单,黑名单,电子邮件签名和不同的机器学习方法,可以解决这个问题。这些解决方案可用于最终用户,但由于网络的动态性质,世界上没有100%的安全系统可以处理这个问题。在大多数情况下,垃圾邮件检测器使用机器学习技术来过滤Web流量。这项工作侧重于系统地分析目前垃圾邮件检测技术的强度和弱点以及已知方法的分类。

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