Detection of Phishing URL using Bayesian Optimized SVM Classifier

Shrishti Shukla, Pratyush Sharma
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引用次数: 2

Abstract

This paper aims to collect, map and model elements that will lead to the finding of phishing UR automatically, for this purpose data mining is used as a basic tool and in this sense, it is considered that the existing patterns in a URL will make it possible to distinguish the legitimate link for pages. Whereas, the identification of these patterns will serve to model a successful classification method. For this purpose, the attributes found in the database “phishing web” that correspond to patterns of phishing pages will be validated, at the same time it will be evaluated algorithms extracted from the literature that allow a better classification of records, finally, a model with the highest precision results is delivered and it consists of Bayesian optimized support vector machine classifier.
基于贝叶斯优化SVM分类器的钓鱼URL检测
本文旨在收集、映射和建模自动发现网络钓鱼UR的元素,为此使用数据挖掘作为基本工具,从这个意义上说,认为URL中的现有模式将使区分页面的合法链接成为可能。然而,这些模式的识别将有助于建立一个成功的分类方法。为此,将验证数据库“phishing web”中发现的与网络钓鱼页面模式相对应的属性,同时对从文献中提取的算法进行评估,使其能够更好地对记录进行分类,最后给出一个精度最高的模型,该模型由贝叶斯优化的支持向量机分类器组成。
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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