E-mail Spam Detection and Phishing link Detection Using Machine Learning

Keerthika J, Adisvara A, Akash S, Jayanesh B, Arul Prakash T
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Abstract

Phishing, which tricks individuals into revealing delicatedata like login credentials and financial details, is the most widespread type of cybercrime. Attackers typically use electronic mail, prompt messaging, and telephone calls to initiate these attacks. Despite ongoing efforts to prevent phishing attacks, current measures are not entirely effective, as the amount of phishing emails has enlarged significantly in current years. While numerous methods have been developed to filter out phishing emails, there is still a need for a comprehensive solution. This survey is the first of its kind to examine the use of N-L-P and ML methods for identifying phishing electronic mail. The analyzesof state_of_the_art N- L-P approaches that are presently being used to detect phishing electronic mail at different periods of the outbreak, with a focus on M-L methods. These methods are compared and evaluated in-depth.
使用机器学习的垃圾邮件检测和网络钓鱼链接检测
网络钓鱼是最普遍的一种网络犯罪,它欺骗个人泄露登录凭证和财务信息等敏感数据。攻击者通常使用电子邮件、提示消息传递和电话呼叫来发起这些攻击。尽管一直在努力防止网络钓鱼攻击,但目前的措施并不完全有效,因为近年来网络钓鱼邮件的数量显著增加。虽然已经开发了许多方法来过滤网络钓鱼电子邮件,但仍然需要一个全面的解决方案。这项调查是第一次研究使用N-L-P和ML方法来识别网络钓鱼电子邮件。当前用于检测网络钓鱼电子邮件爆发不同时期的最新N- L-P方法的分析,重点是M-L方法。对这些方法进行了深入的比较和评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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