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Data mining a way to solve Phishing Attacks

机译:数据挖掘是解决网络钓鱼攻击的一种方法

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摘要

With the ever increasing use of Internet by different stake holders in various fields, information on web browsers and servers is highly susceptible to different security attacks. Though high security measures and enhanced techniques are used to protect the information on the web browsers and servers, they are still prone to a number of attacks. Phishing is one such type of attack in which users are tricked by the phishers using social engineering methods to steal their personal or confidential information. Detection of phishing attack with high accuracy is a challenging research issue. Users are duped by the phishers to enter their confidential information into websites created by them and thereby are steal the vital user's credentials. Phishing sites are normally detected by using blacklist based approach but this approach fails as white listed phishing sites cannot be detected using this approach. This research work aims to use data mining algorithms to analyze E-mails and also helps in preventing phishing attacks. This paper proposed an architectural model to differentiate between the fake E-mail and real E-mail with a high accuracy and use naive Bayesian classification for the said purpose. The proposed algorithm works in various stages for fake E-mail detection and hence tries to protect the users from leaking their confidential information.
机译:随着不同利益相关方在各个领域对Internet的日益增长的使用,Web浏览器和服务器上的信息极易受到各种安全攻击的攻击。尽管使用了高安全性措施和增强的技术来保护Web浏览器和服务器上的信息,但它们仍然容易受到多种攻击。网络钓鱼是一种这样的攻击,其中网络钓鱼者使用社交工程方法欺骗用户,以窃取其个人或机密信息。高精度检测网络钓鱼攻击是一个具有挑战性的研究问题。网络钓鱼者诱骗用户将其机密信息输入由他们创建的网站,从而窃取重要用户的凭据。通常使用基于黑名单的方法检测网络钓鱼站点,但是此方法失败,因为使用此方法无法检测到白名单的网络钓鱼站点。这项研究工作旨在使用数据挖掘算法来分析电子邮件,还有助于防止网络钓鱼攻击。本文提出了一种体系结构模型,该模型可以高精度地区分伪造的电子邮件和真实的电子邮件,并为此目的使用朴素的贝叶斯分类。所提出的算法在伪造电子邮件检测的各个阶段均有效,因此试图保护用户免于泄露其机密信息。

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