基于随机森林、支持向量机和反向传播神经网络的网络钓鱼检测

S. Sindhu, Sunil Parameshwar Patil, Arya Sreevalsan, F. Rahman, Ms. Saritha A. N.
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引用次数: 11

摘要

网络钓鱼是一种常见的攻击,利用视觉上与合法网站相似的网站获取敏感信息。随着技术的发展,网络钓鱼攻击呈上升趋势。机器学习是一种非常流行的检测网络钓鱼网站的方法。本文解释了现有的用于检测网络钓鱼网站的机器学习方法。本文介绍了改进的随机森林分类方法、支持向量机分类算法和神经网络反向传播分类方法,实现的准确率分别为97.369%、97.451%和97.259%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Phishing Detection using Random Forest, SVM and Neural Network with Backpropagation
Phishing is a common attack used to obtain sensitive information using visually similar websites to that of legitimate websites. With the growing technology, phishing attacks are on the rise. Machine Learning is a very popular approach to detect phishing websites. This paper explains the existing machine learning methods that are used to detect phishing websites. The paper explains the improved Random Forest classification method, SVM classification algorithm and Neural Network with backpropagation classification methods which have been implemented with accuracies of 97.369%, 97.451% and 97.259% respectively.
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