基于极限机器学习的钓鱼网站检测

M. Lakshmi
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引用次数: 0

摘要

网络钓鱼网站通过引诱受害者浏览一个类似于诚实善良的虚假网站页面来获取受害者的机密数据,这是通过互联网进行的另一种犯罪行为,它是电子账户管理和零售等许多领域特别关注的问题之一。网络钓鱼网站检测确实是一个不可预测的问题,包括许多不稳定的组件和标准。由于程序员所使用的智能程序在安排站点时存在歧义,因此可以使用一些敏锐的主动策略和强大的工具,例如模糊、神经系统和数据挖掘方法可以作为识别网络钓鱼站点的成功机制。关键词:极限学习机,特征分类,信息安全,网络钓鱼。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of Phishing Websites Based on Extreme Machine Learning
Phishing sites which expects to take the victims confidential data by diverting them to surf a fake website page that resembles a honest to goodness one is another type of criminal acts through the internet and its one of the especially concerns toward numerous areas including e-managing an account and retailing. Phishing site detection is truly an unpredictable and element issue including numerous components and criteria that are not stable. On account of the last and in addition ambiguities in arranging sites because of the intelligent procedures programmers are utilizing, some keen proactive strategies can be helpful and powerful tools can be utilized, for example, fuzzy, neural system and data mining methods can be a successful mechanism in distinguishing phishing sites. Keyword: - Extreme Learning Machine, Features Classification, Information Security, Phishing.
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