A Support Vector Machine Learning Technique for Detection of Phishing Websites

Saumya Jain, Chetan Gupta
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引用次数: 1

Abstract

In the field of cyber attacks, phishing is considered to be one of the pioneers. Phishing sites are websites that look and sound like legitimate ones but are really scams. For the purpose of duping the target into thinking its real, they are manufactured. The phishing scams of today are more complex and dangerous than ever before. Phishing website detection may be achieved by using machine and deep learning approaches based on artificial intelligence. It is possible to use the ML classification algorithm for phishing website detection. This paper presents a support vector machine learning technique for the detection of phishing websites. Simulation is performed using spyder IDE. Simulation results provide better accuracy.
网络钓鱼网站检测的支持向量机器学习技术
在网络攻击领域,网络钓鱼被认为是先驱之一。网络钓鱼网站看起来和听起来像合法的网站,但实际上是骗局。为了使目标相信它是真实的,它们被制造出来。今天的网络钓鱼诈骗比以往任何时候都更加复杂和危险。网络钓鱼网站检测可以通过使用基于人工智能的机器和深度学习方法来实现。可以使用ML分类算法进行网络钓鱼网站检测。本文提出了一种用于网络钓鱼网站检测的支持向量机器学习技术。使用spyder IDE进行仿真。仿真结果提供了更好的精度。
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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